79 Tensorflow Serving Jobs

As an AI Integration Engineer at our company in Ahmedabad, your role will involve deploying and integrating AI/ML models into production environments to ensure scalability, reliability, and security. You will collaborate with various teams to streamline AI adoption and maximize the impact of our AI-driven solutions. Key Responsibilities: - Collaborate with AI/ML teams to deploy machine learning models into applications and services. - Design and implement APIs, SDKs, and microservices to facilitate seamless AI model consumption. - Maintain high availability, scalability, and security of AI services in production. - Implement CI/CD pipelines, observability tools, and monitoring systems to track and enhance model performance. - Develop automated pipelines for model retraining, versioning, and rollback. - Apply caching strategies, model compression, and other techniques to optimize inference speed and system efficiency. - Work with cloud platforms (AWS, GCP, Azure) and support hybrid setups (cloud + on-prem). - Partner with DevOps, software engineers, and product teams to smoothly integrate AI capabilities into products and services. Required Qualifications: - Bachelors or Masters degree in Computer Science, Engineering, or a related field. - 3 - 6 years of experience in software engineering, backend development, or MLOps. - Proficiency in Python with experience using FastAPI, Flask, or Django. - Experience integrating ML models with tools like TensorFlow Serving, TorchServe, ONNX, or custom model wrappers. - Hands-on experience with Docker, Kubernetes, and container orchestration. - Understanding of CI/CD pipelines, MLOps practices, and model lifecycle management. - Familiarity with cloud platforms (AWS, GCP, Azure) and their AI/ML services. - Excellent English communication skills to collaborate with cross-functional teams and present technical solutions clearly.,

As a Senior Backend Engineer at our company, you will be responsible for designing, developing, and maintaining the infrastructure powering our generative AI applications. You will collaborate with AI engineers, platform teams, and product stakeholders to build scalable and reliable backend systems supporting AI model deployment, inference, and integration. This role will challenge you to combine traditional backend engineering expertise with cutting-edge AI infrastructure challenges to deliver robust solutions at enterprise scale. - Design and implement scalable backend services and APIs for generative AI applications using microservices architecture and cloud-native patterns. - Build and maintain model serving infrastructure with load balancing, auto-scaling, caching, and failover capabilities for high-availability AI services. - Deploy and orchestrate containerized AI workloads using Docker, Kubernetes, ECS, and OpenShift across development, staging, and production environments. - Develop serverless AI functions using AWS Lambda, ECS Fargate, and other cloud services for scalable, cost-effective inference. - Implement robust CI/CD pipelines for automated deployment of AI services, including model versioning and gradual rollout strategies. - Create comprehensive monitoring, logging, and alerting systems for AI service performance, reliability, and cost optimization. - Integrate with various LLM APIs (OpenAI, Anthropic, Google) and open-source models, implementing efficient batching and optimization techniques. - Build data pipelines for training data preparation, model fine-tuning workflows, and real-time streaming capabilities. - Ensure adherence to security best practices, including authentication, authorization, API rate limiting, and data encryption. - Collaborate with AI researchers and product teams to translate AI capabilities into production-ready backend services. - Strong experience with backend development using Python, with familiarity in Go, Node.js, or Java for building scalable web services and APIs. - Hands-on experience with containerization using Docker and orchestration platforms including Kubernetes, OpenShift, and AWS ECS in production environments. - Proficient with cloud infrastructure, particularly AWS services (Lambda, ECS, EKS, S3, RDS, ElastiCache) and serverless architectures. - Experience with CI/CD pipelines using Jenkins, GitLab CI, GitHub Actions, or similar tools, including Infrastructure as Code with Terraform or CloudFormation. - Strong knowledge of databases including PostgreSQL, MongoDB, Redis, and experience with vector databases for AI applications. - Familiarity with message queues (RabbitMQ, Apache Kafka, AWS SQS/SNS) and event-driven architectures. - Experience with monitoring and observability tools such as Prometheus, Grafana, DataDog, or equivalent platforms. - Knowledge of AI/ML model serving frameworks like MLflow, Kubeflow, TensorFlow Serving, or Triton Inference Server. - Understanding of API design principles, load balancing, caching strategies, and performance optimization techniques. - Experience with microservices architecture, distributed systems, and handling high-traffic, low-latency applications. - Bachelors degree in computer science, Engineering, or related technical field, or equivalent practical experience. - 4+ years of experience in backend engineering with focus on scalable, production systems. - 2+ years of hands-on experience with containerization, Kubernetes, and cloud infrastructure in production environments. - Demonstrated experience with AI/ML model deployment and serving in production systems.,

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you'd like, where you'll be supported and inspired by a collaborative community of colleagues around the world, and where you'll be able to reimagine what's possible. Join us and help the world's leading organizations unlock the value of technology and build a more sustainable, more inclusive world. Your Role Design, implement, and maintain end-to-end ML pipelines for model training, evaluation, and deployment Collaborate with data scientists and software engineers to operationalize ML models, serving frameworks (TensorFlow Serving, TorchServe) and experience with MLOps tools Develop and maintain CI/CD pipelines for ML workflows Implement monitoring and logging solutions for ML models, experience with ML model serving frameworks (TensorFlow Serving, TorchServe) Optimize ML infrastructure for performance, scalability, and cost-efficiency Your Profile Strong programming skills in Python (5+ years), with experience in ML frameworks understanding of ML-specific testing and validation techniques Expertise in containerization technologies (Docker) and orchestration platforms (Kubernetes), Knowledge of data versioning and model versioning techniques Proficiency in cloud platform (AWS) and their ML-specific services with atleast 2-3 years of experience. Strong understanding of DevOps practices and tools (GitLab, Artifactory, Gitflow etc.) Experience with monitoring and observability tools (Prometheus, Grafana, ELK stack) and knowledge of distributed training techniques What you'll love about working here We recognise the significance of flexible work arrangements to provide support in hybrid mode, you will get an environment to maintain healthy work life balance Our focus will be your career growth & professional development to support you in exploring the world of opportunities. Equip yourself with valuable certifications & training programmes in the latest technologies such as MLOps, Machine Learning Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.

JOB TITLE: AI ASSOCIATE OPTIMIZATION Location: Bengaluru | Experience: 1-3 Years ABOUT NEWCOLD NewCold is a service provider in cold chain logistics with a focus on development and operation of large, highly automated cold stores. NewCold strives to be crucial in the cold chain of leading food companies, by offering advanced logistic services worldwide. NewCold is one of the fastest growing companies (over 2,000 employees) in the cold chain logistics and they are expanding teams to support this growth. They use the latest technology that empowers people, to handle food responsibly and guarantee food safety in a sustainable way. They challenge the industry, believe in long-term partnerships, and deliver solid investment opportunities that enable next generation logistic solutions. NewCold has leading market in-house expertise in designing, engineering, developing and operating state-of-the-art automated cold stores: a result of successful development and operation of over 15 automated warehouses across three continents. With the prospect of many new construction projects around the world in the very near future, this vacancy offers an interesting opportunity to join an internationally growing and ambitious organization. POSITION SUMMARY NewCold is seeking an AI Associate Optimization to enhance the performance and efficiency of our AI-powered solutions within our highly automated cold chain logistics network. This role focuses on optimizing machine learning models deployed in warehouse operations, ensuring low latency, high throughput, and accurate predictions for improved decision-making. You will be instrumental in bridging the gap between data science and real-world deployment, contributing to the continuous improvement of our automated systems. This position requires a strong understanding of model serving, containerization, and edge AI technologies. YOUR ROLE As an AI Associate Optimization, you will play a critical role in ensuring the reliability, scalability, and performance of AI models powering NewColds automated warehouse processes. You will be responsible for optimizing models for deployment across diverse infrastructure, including cloud and edge environments, directly impacting operational efficiency, cost reduction, and the overall effectiveness of our logistics solutions. Your work will contribute to maintaining NewColds competitive edge through cutting-edge AI implementation. KEY RESPONSIBILITIES Implement model optimization techniques such as quantization and knowledge distillation to reduce model size and improve inference speed for deployment on edge devices and cloud infrastructure. Develop and maintain CI/CD pipelines for automated model deployment and updates, ensuring seamless integration with existing systems. Benchmark and profile model performance (latency, throughput, memory usage) to identify bottlenecks and areas for improvement. Deploy and manage machine learning models using model serving frameworks like TensorFlow Serving, TorchServe, ONNX Runtime, or Triton Inference Server. Containerize AI models and applications using Docker and Podman for consistent and reproducible deployments. Collaborate with data scientists and software engineers to troubleshoot model performance issues and implement solutions. Monitor model performance in production and proactively address any degradation in accuracy or efficiency. Develop and maintain APIs/SDKs (REST, gRPC, FastAPI) for accessing and integrating AI models into various applications. Work with edge devices (NVIDIA Jetson, Coral TPU, ARM-based boards) and edge frameworks (TensorRT, OpenVINO, TFLite, TVM) to optimize models for low-power, real-time inference. WHAT WE ARE LOOKING FOR Bachelors or masters degree in computer science, Artificial Intelligence, Machine Learning or a related field 1-3 years of experience in a role focused on machine learning model optimization and deployment. Proficiency in Python and C++ programming languages. Hands-on experience with model serving frameworks (TensorFlow Serving, TorchServe, ONNX Runtime, Triton Inference Server). Experience with containerization technologies (Docker, Podman) and orchestration tools (Kubernetes, K3s, Edge orchestrators). Knowledge of model optimization techniques such as quantization and knowledge distillation. Familiarity with benchmarking and profiling tools for evaluating model performance. Strong analytical and problem-solving skills with a data-driven approach. Experience with CI/CD pipelines for ML deployment is highly desirable. Knowledge of edge devices (NVIDIAJetson, Coral TPU, ARM-based boards) and edge AI frameworks (TensorRT, OpenVINO, TFLite, TVM) is a significant plus. WHY JOIN US Opportunity to work on cutting-edge AI applications in a rapidly growing and innovative cold chain logistics company. Exposure to a wide range of AI technologies and challenges within a highly automated warehouse environment. Career growth potential within a dynamic and international organization. Collaborative and supportive team environment with opportunities for learning and development. Contribute to the development of next-generation logistics solutions that are shaping the future of the food supply chain. Show more Show less

Job Description * Role & Responsibilities: Deploy and manage Kubernetes clusters with autoscaling using Karpenter . Implement event-driven scaling with KEDA for dynamic workloads. Optimize GPU workloads for compute-intensive tasks in Kubernetes. Build and maintain Grafana dashboards with Prometheus integration. Develop Python scripts for automation, IaC, and CI/CD pipelines. Manage containerized deployments with Docker and Helm . Collaborate on CI/CD workflows using Jenkins and GitLab. Troubleshoot production issues ensuring high availability . Mentor junior engineers and enforce DevOps best practices. Must-Have Skills: Golang (Mandatory) and Python expertise. Strong hands-on with KEDA , Kubernetes, Docker, Helm. CI/CD exposure with Jenkins / GitLab. Knowledge of Grafana, Prometheus for monitoring. Good-to-Have Skills: AI/ML architecture knowledge (neural networks, inference). Experience with ML deployment tools ( Kubeflow, TensorFlow Serving ). Familiarity with ML model development ( PyTorch, scikit-learn ).

Location: Mumbai, India Experience Level: 9 Plus Years Minimum Qualification: Masters Degree in Computer Science, Engineering, or related field. About the Role: Were looking for a strategic Senior MLOps Engineer to lead the end-to-end design, implementation, and scaling of our AI infrastructure. Youll partner with researchers, product teams, and DevOps to turn prototypes into production services that meet strict SLAs for latency, reliability, and cost efficiency. Responsibilities: Core MLOps Pipelines: Design and implement scalable ML pipelines (training, evaluation, deployment) for LLMs, CV, and multimodal models . Model Serving & CI/CD: Lead efforts in model serving, versioning, automated CI/CD, and real-time monitoring of AI workflows . Inference-as-a-Service: Build and optimize GPU-backed serving infrastructure targeting p99 latency < 100 ms, 99.9% uptime, and > 80% GPU utilization . Governance & Drift Detection: Drive initiatives on model governance, automated drift detection (?10% false positives), and data-management best practices . Vector Search & Agent Orchestration: Integrate vector databases (Qdrant, Pinecone) for low-latency semantic retrieval, and build agentic workflows using LangChain or similar frameworks. Enterprise Multi-Tenancy: Architect RBAC-driven, isolated ML services to securely serve 100500+ organizations. Observability & Logging: Design Prometheus/Grafana dashboards, ELK/Fluentd logging pipelines, and alerting for all ML workloads. CI/CD for Inference APIs: Maintain CI/CD pipelines for Python (FastAPI) and TypeScript (NestJS) inference services. Metrics & Cost Optimization: Define and track SLAs/SLOs, optimize cloud spend by ? 20% year-over-year, and ensure GPU clusters operate at > 80% utilization. Cross-Functional Leadership: Partner with AI researchers, product managers, and legal to align MLOps standards with compliance and roadmap goals. Mentorship & Community: Mentor junior engineers, run quarterly brown-bags, own onboarding docs (upskill 5+ engineers/quarter), and publish ? 1 open-source contribution or talk annually. Requirements : 914 years in software engineering, including ? 4 years in MLOps or ML infrastructure Strong expertise in cloud platforms (AWS/GCP/Azure), Kubernetes, Docker, Terraform, Helm, Kubeflow, and MLflow Experience with inference frameworks (Triton, TensorFlow Serving, BentoML, TorchServe) Familiarity with distributed training, workload schedulers, and GPU-cluster orchestration Proficiency in Python, TypeScript, and infrastructure-as-code (Terraform, Helm, etc.) Proven track record building reliable, scalable ML systems in production. Plus These Critical Skills: Vector DB integration (Qdrant, Pinecone) Agent orchestration (LangChain, LlamaIndex) Multi-tenant security and RBAC Observability stacks (Prometheus/Grafana, ELK) CI/CD for FastAPI/NestJS services Preferred : Masters/PhD in CS/AI and certifications such as AWS ML Specialty, Google Cloud Professional ML Engineer, or CNCF CKA/CKAD. Prior experience at AI-focused startups or enterprises scaling ML for 100500 orgs. Understanding of low-latency streaming inference or agent-based LLM systems. Excellent written and verbal communication, and a proven ability to drive consensus across functions. Show more Show less

Job Description * Role & Responsibilities: Deploy and manage Kubernetes clusters with autoscaling using Karpenter . Implement event-driven scaling with KEDA for dynamic workloads. Optimize GPU workloads for compute-intensive tasks in Kubernetes. Build and maintain Grafana dashboards with Prometheus integration. Develop Python scripts for automation, IaC, and CI/CD pipelines. Manage containerized deployments with Docker and Helm . Collaborate on CI/CD workflows using Jenkins and GitLab. Troubleshoot production issues ensuring high availability . Mentor junior engineers and enforce DevOps best practices. Must-Have Skills: Golang (Mandatory) and Python expertise. Strong hands-on with KEDA , Kubernetes, Docker, Helm. CI/CD exposure with Jenkins / GitLab. Knowledge of Grafana, Prometheus for monitoring. Good-to-Have Skills: AI/ML architecture knowledge (neural networks, inference). Experience with ML deployment tools ( Kubeflow, TensorFlow Serving ). Familiarity with ML model development ( PyTorch, scikit-learn ).

As an Artificial Intelligence/Machine Learning Expert, you will be responsible for developing and maintaining web applications using Django and Flask frameworks. You will design and implement RESTful APIs using Django Rest Framework (DRF) and deploy, manage, and optimize applications on AWS services such as EC2, S3, RDS, Lambda, and CloudFormation. Your role will involve building and integrating APIs for AI/ML models into existing systems and creating scalable machine learning models using frameworks like PyTorch, TensorFlow, and scikit-learn. You will implement transformer architectures (e.g., BERT, GPT) for NLP and other advanced AI use cases and optimize machine learning models through techniques like hyperparameter tuning, pruning, and quantization. Additionally, you will be responsible for deploying and managing machine learning models in production environments using tools like TensorFlow Serving, TorchServe, and AWS SageMaker. It will be crucial for you to ensure the scalability, performance, and reliability of applications and deployed models. Collaboration with cross-functional teams to analyze requirements and deliver effective technical solutions will be an essential part of your responsibilities. You will also be expected to write clean, maintainable, and efficient code following best practices, conduct code reviews, and provide constructive feedback to peers. Staying up-to-date with the latest industry trends and technologies, particularly in AI/ML, will be necessary to excel in this role.,

Presage Insights is a cutting-edge startup specializing in predictive maintenance. The platform leverages AI-powered diagnostics to detect and diagnose machine failures, integrating with an inbuilt CMMS (Computerized Maintenance Management System) to optimize industrial operations. The company is expanding its capabilities in time series analysis and LLM-powered insights. As a Machine Learning Intern at Presage Insights, you will work on real-world industrial datasets, applying AI techniques to extract insights and enhance predictive analytics capabilities. Your role will involve collaborating with the engineering team to develop, fine-tune, and deploy LLM-based models for anomaly detection, trend prediction, and automated report generation. Key Responsibilities: - Research and implement state-of-the-art LLMs for natural language processing tasks related to predictive maintenance. - Apply machine learning techniques for time series analysis, including anomaly detection and clustering. - Fine-tune pre-trained LLMs (GPT, LLaMA, Mistral, etc.) for domain-specific applications. - Develop pipelines for processing and analyzing vibration and sensor data. - Integrate ML models into the existing platform and optimize inference efficiency. - Collaborate with software engineers and data scientists to improve ML deployment workflows. - Document findings, methodologies, and contribute to technical reports. Required Qualifications: - Pursuing or completed a degree in Computer Science, Data Science, AI, or a related field. - Hands-on experience with LLMs and NLP frameworks such as Hugging Face Transformers, LangChain, OpenAI API, or similar. - Strong proficiency in Python, PyTorch, TensorFlow, or JAX. - Solid understanding of time series analysis, anomaly detection, and signal processing. - Familiarity with vector databases (FAISS, Pinecone, ChromaDB) and prompt engineering. - Knowledge of ML model deployment (Docker, FastAPI, or TensorFlow Serving) is a plus. - Ability to work independently and adapt to a fast-paced startup environment. What You'll Gain: - Hands-on experience working with LLMs & real-world time series data. - Opportunity to work at the intersection of AI and predictive maintenance. - Mentorship from industry experts in vibration analysis and AI-driven diagnostics. - Potential for a full-time role based on performance. Note: This is an unpaid internship.,

You will be joining the S&C Global Network team at Accenture as an AI CMT - ML Architecture Specialist, based in Bengaluru, BDC7C. Your primary responsibility will be to design machine learning architectures and drive strategic initiatives to create value-driven solutions for business transformations. In this role, you will provide strategic advisory services, conduct market research, and develop data-driven recommendations to enhance business performance. You will also be involved in conducting ML maturity assessments, identifying areas for improvement, and providing strategic recommendations to enhance the overall ML capability aligned with business objectives. As part of your responsibilities, you will develop ML Ops roadmaps, establish robust processes for the end-to-end machine learning lifecycle, and implement best practices to ensure the efficiency, scalability, and reliability of ML systems. Additionally, you will lead the design and implementation of Generative AI solutions, staying updated with the latest advancements in the field. Your role will also involve proactively identifying opportunities for ML and Gen AI applications within the organization, collaborating with cross-functional teams to design bespoke ML solutions, and providing technical leadership and mentorship to team members. Strong collaboration with business stakeholders and effective communication of complex technical concepts to non-technical audiences will be essential. To be successful in this role, you should have relevant experience in the domain, strong analytical and problem-solving skills, and the ability to work in a fast-paced environment. Proficiency in deep learning frameworks such as TensorFlow and PyTorch, hands-on experience in operationalizing machine learning systems, and expertise in ML Ops best practices are required. Experience in Telecom, Hi-Tech, or Software and platform industry is desirable. You will have the opportunity to work on innovative projects, receive continuous learning and growth support from Accenture, and be part of a diverse global community focused on pushing the boundaries of business capabilities. Your career growth and leadership exposure will be nurtured within the organization. In summary, as an AI CMT - ML Architecture Specialist at Accenture, you will play a key role in driving strategic initiatives, managing business transformations, and implementing cutting-edge ML and Gen AI solutions to enhance business performance and capabilities.,

We are seeking a highly motivated and experienced AI/ML Lead to join our team. In this role, you will lead and manage software development projects across diverse domains such as Gaming, Banking, Fintech, E-commerce, Logistics, Healthcare, and On-Demand Services. You will be responsible for ensuring the successful delivery of projects, meeting deadlines, and exceeding client expectations. Startup Experience: Proven ability to take extreme ownership of results, with a history of leaving a lasting impact on the business. Relevant Experience: At least 5-9 years of experience demonstrated the ability to develop resilient, high-performance, and scalable code tailored to application usage demands. Node.js Framework Expertise: Significant experience in designing and building Node services with Expressjs, NestJS and Fastify, with proficiency in JavaScript & Typescript. REST API & GraphQL: Hands-on expertise in development API endpoints with RESTful & GraphQL approach. Deep expertise in Python, ML Algorithms and ML frameworks such as TensorFlow, PyTorch, scikit-learn , etc. Strong understanding of cloud-based ML platforms (e.g., AWS SageMaker, Azure ML, GCP Vertex AI ). Proven experience building production-grade ML systems at scale. Experience with MLOps tools and practices: model versioning, CI/CD, monitoring, and retraining. Solid software engineering background: Git, containerization (Docker), microservices, APIs.

Ready to build the future with AI At Genpact, we don&rsquot just keep up with technology&mdashwe set the pace. AI and digital innovation are redefining industries, and we&rsquore leading the charge. Genpact&rsquos , our industry-first accelerator, is an example of how we&rsquore scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to , our breakthrough solutions tackle companies most complex challenges. If you thrive in a fast-moving, innovation-driven environment, love building and deploying cutting-edge AI solutions, and want to push the boundaries of what&rsquos possible, this is your moment. Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions - we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation , our teams implement data, technology, and AI to create tomorrow, today. Get to know us at and on , , , and . Inviting applications for the role of Business Analyst , Data Scientist In this role, w e are seeking a highly skilled ML CV Ops Engineer to join our AI Engineering team. This role is focused on operationalizing Computer Vision models&mdashensuring they are efficiently trained, deployed, monitored , and retrained across scalable infrastructure or edge environments. The ideal candidate has deep technical knowledge of ML infrastructure, DevOps practices, and hands-on experience with CV pipelines in production. You&rsquoll work closely with data scientists, DevOps, and software engineers to ensure computer vision models are robust, secure, and production-ready always. Responsibilities: End-to-End Pipeline Automation: Build and maintain ML pipelines for computer vision tasks (data ingestion, preprocessing, model training, evaluation, inference). Use tools like MLflow , Kubeflow, DVC, and Airflow to automate workflows. Model Deployment & Serving: Package and deploy CV models using Docker and orchestration platforms like Kubernetes. Use model-serving frameworks (TensorFlow Serving, TorchServe , Triton Inference Server) to enable real-time and batch inference. Monitoring & Observability: Set up model monitoring to detect drift, latency spikes, and performance degradation. Integrate custom metrics and dashboards using Prometheus, Grafana, and similar tools. Model Optimization: Convert and optimize models using ONNX, TensorRT , or OpenVINO for performance and edge deployment. Implement quantization, pruning, and benchmarking pipelines. Edge AI Enablement (Optional but Valuable): Deploy models on edge devices (e.g., NVIDIA Jetson, Coral, Raspberry Pi) and manage updates and logs remotely. Collaboration & Support: Partner with Data Scientists to productionize experiments and guide model selection based on deployment constraints. Work with DevOps to integrate ML models into CI/CD pipelines and cloud-native architecture. Qualifications we seek in you! Minimum Qualifications: Bachelor&rsquos or master&rsquos in computer science , Engineering, or a related field. Sound experience in ML engineering, with significant work in computer vision and model operations. Strong coding skills in Python and familiarity with scripting for automation. Hands-on experience with PyTorch , TensorFlow, OpenCV, and model lifecycle tools like MLflow , DVC, or SageMaker. Solid understanding of containerization and orchestration (Docker, Kubernetes). Experience with cloud services (AWS/GCP/Azure) for model deployment and storage. Preferred Qualifications: Experience with real-time video analytics or image-based inference systems. Knowledge of MLOps best practices (model registries, lineage, versioning). Familiarity with edge AI deployment and acceleration toolkits (e.g., TensorRT , DeepStream ). Exposure to CI/CD pipelines and modern DevOps tooling (Jenkins, GitLab CI, ArgoCD ). Contributions to open-source ML/CV tooling or experience with labeling workflows (CVAT, Label Studio). Why join Genpact Lead AI-first transformation - Build and scale AI solutions that redefine industries Make an impact - Drive change for global enterprises and solve business challenges that matter Accelerate your career &mdashGain hands-on experience, world-class training, mentorship, and AI certifications to advance your skills Grow with the best - Learn from top engineers, data scientists, and AI experts in a dynamic, fast-moving workplace Committed to ethical AI - Work in an environment where governance, transparency, and security are at the core of everything we build Thrive in a values-driven culture - Our courage, curiosity, and incisiveness - built on a foundation of integrity and inclusion - allow your ideas to fuel progress Come join the 140,000+ coders, tech shapers, and growth makers at Genpact and take your career in the only direction that matters: Up. Let&rsquos build tomorrow together. Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color , religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation. Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a %27starter kit,%27 paying to apply, or purchasing equipment or training.

Ready to shape the future of work At Genpact, we don&rsquot just adapt to change&mdashwe drive it. AI and digital innovation are redefining industries, and we&rsquore leading the charge. Genpact&rsquos , our industry-first accelerator, is an example of how we&rsquore scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to , our breakthrough solutions tackle companies most complex challenges. If you thrive in a fast-moving, tech-driven environment, love solving real-world problems, and want to be part of a team that&rsquos shaping the future, this is your moment. Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions - we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation , our teams implement data, technology, and AI to create tomorrow, today. Get to know us at and on , , , and . Inviting applications for the role of Lead Consultant - ML/CV Ops Engineer ! We are seeking a highly skilled ML CV Ops Engineer to join our AI Engineering team. This role is focused on operationalizing Computer Vision models&mdashensuring they are efficiently trained, deployed, monitored , and retrained across scalable infrastructure or edge environments. The ideal candidate has deep technical knowledge of ML infrastructure, DevOps practices, and hands-on experience with CV pipelines in production. You&rsquoll work closely with data scientists, DevOps, and software engineers to ensure computer vision models are robust, secure, and production-ready always. Key Responsibilities: End-to-End Pipeline Automation: Build and maintain ML pipelines for computer vision tasks (data ingestion, preprocessing, model training, evaluation, inference). Use tools like MLflow , Kubeflow, DVC, and Airflow to automate workflows. Model Deployment & Serving: Package and deploy CV models using Docker and orchestration platforms like Kubernetes. Use model-serving frameworks (TensorFlow Serving, TorchServe , Triton Inference Server) to enable real-time and batch inference. Monitoring & Observability: Set up model monitoring to detect drift, latency spikes, and performance degradation. Integrate custom metrics and dashboards using Prometheus, Grafana, and similar tools. Model Optimization: Convert and optimize models using ONNX, TensorRT , or OpenVINO for performance and edge deployment. Implement quantization, pruning, and benchmarking pipelines. Edge AI Enablement (Optional but Valuable): Deploy models on edge devices (e.g., NVIDIA Jetson, Coral, Raspberry Pi) and manage updates and logs remotely. Collaboration & Support: Partner with Data Scientists to productionize experiments and guide model selection based on deployment constraints. Work with DevOps to integrate ML models into CI/CD pipelines and cloud-native architecture. Qualifications we seek in you! Minimum Qualifications Bachelor&rsquos or Master&rsquos in Computer Science , Engineering, or a related field. Sound experience in ML engineering, with significant work in computer vision and model operations. Strong coding skills in Python and familiarity with scripting for automation. Hands-on experience with PyTorch , TensorFlow, OpenCV, and model lifecycle tools like MLflow , DVC, or SageMaker. Solid understanding of containerization and orchestration (Docker, Kubernetes). Experience with cloud services (AWS/GCP/Azure) for model deployment and storage. Preferred Qualifications: Experience with real-time video analytics or image-based inference systems. Knowledge of MLOps best practices (model registries, lineage, versioning). Familiarity with edge AI deployment and acceleration toolkits (e.g., TensorRT , DeepStream ). Exposure to CI/CD pipelines and modern DevOps tooling (Jenkins, GitLab CI, ArgoCD ). Contributions to open-source ML/CV tooling or experience with labeling workflows (CVAT, Label Studio). Why join Genpact Be a transformation leader - Work at the cutting edge of AI, automation, and digital innovation Make an impact - Drive change for global enterprises and solve business challenges that matter Accelerate your career - Get hands-on experience, mentorship, and continuous learning opportunities Work with the best - Join 140,000+ bold thinkers and problem-solvers who push boundaries every day Thrive in a values-driven culture - Our courage, curiosity, and incisiveness - built on a foundation of integrity and inclusion - allow your ideas to fuel progress Come join the tech shapers and growth makers at Genpact and take your career in the only direction that matters: Up. Let&rsquos build tomorrow together. Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color , religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation. Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a %27starter kit,%27 paying to apply, or purchasing equipment or training.

As a Senior Data Scientist with expertise in Deep Learning and NLP, you will be responsible for researching, designing, and developing core Artificial Intelligence (AI) and Machine Learning (ML) algorithms. Your essential skills will include working experience with various AI frameworks, such as CRFs, BERT/Transformers, and text summarization models. You should have past experience in Entity Detection and Disambiguation, Intent Detection, and deploying machine learning models in a production environment using tools like TensorFlow Serving. Your role will involve collaborating with functional domain experts, implementation teams, and AI backend teams to ensure efficient product development, production release, and development cycles. It is crucial to stay updated with the latest AI developments, including training libraries/frameworks, Reinforcement Learning, and Deep Learning, by reading and researching state-of-the-art papers in NLU problems. You will work with large datasets of support tickets and unstructured data sources, building Deep Learning models in TensorFlow for GPUs and CPUs. Additionally, collaborating with Backend Engineers to deploy your models to production will be part of your responsibilities. Your background should include a Bachelor's degree in B.E/B.Tech, with a strong industry focus on IT/Computers-Software. You must possess a good understanding of Transfer Learning concepts, Question-Answering Framework, and have experience in building production-ready NLP systems. Familiarity with Distributed systems like Docker, Kubernetes, and Azure, as well as working knowledge of Linux OS, will be beneficial for this role. If you have the passion and skills to work on cutting-edge AI technologies, this Senior Data Scientist position offers a challenging opportunity to contribute to the development of innovative NLP systems in a dynamic environment.,

- Proficiency in LLM systems, prompt fine-tuning - experience with infrastructure management, model deployment, and optimization. - Understanding of cloud architecture, performance and scalability. - Experience with machine learning frameworks Health insurance Provident fund

Job Title: Python Developer (38 Years Experience) Location: Chennai Job Type: Full-Time Work Mode: Office (5 Days) Experience Required: 3 to 8 Years Job Description Ability to perform data transformations using Python, pandas, numpy, polars. Ability to query data from SQL and NoSQL databases. For SQL: Given a database schema should be able to create a query to extract desired data. For NoSQL: Experience performing hierarchical queries in REST or experience with GraphQL. Database operation (indexing etc), cache (redis etc), data management, handle missing data etc. Data visualization in Python (Plotly, Bokeh, matplotlib etc.) Some experience with distributed programming using Spark or Dask or similar toolkits that can take sequential code and run it across multiple processes. Familiar with machine learning, experience with deep learning model analysis, popular architectures, augmentations, fine tune, boosting, dimension reduction etc. Basic knowledge of computer vision and image processing, understand concept of feature extraction, object detection and classification, segmentation, denoise, super resolution etc. Familiar with python / tensorflow / pytorch / openCV etc. Good software skill, knows concept and object-oriented design, and basic software system design Experience of organize and aggregate large datasets Good data analysis and analytic skill

We are looking for 8+years experienced candidates for this role. Job Description A minimum of 8 years of professional experience, with at least 6 years in a data science role. Strong knowledge of statistical modeling, machine learning, deep learning and GenAI. Proficiency in Python and hands on experience optimizing code for performance. Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning. Solid understanding of database concepts and experience working with large datasets. Experience deploying and scaling machine learning models in a production environment. Familiarity with machine learning operations (MLOps) and related tools. Good understanding of Generative AI concepts and LLM finetuning. Excellent communication and collaboration skills. Responsibilities include: Lead a high performance team, guide and mentor them on the latest technology landscape, patterns and design standards and prepare them to take on new roles and responsibilities. Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions. Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance. Lead the development and deployment of machine learning/deep learning models to address key business challenges. Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models. Utilize expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems. Design, implement, and optimize data pipelines for model training and deployment. Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe, KServe, or similar). Design and implement APIs for model serving and integration with other systems. Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results. Mentor junior data scientists, providing guidance on technical skills and project execution. Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications. Communicate complex technical concepts and analytical findings to both technical and non-technical audiences. Serves as a primary point of contact for client managers and liaises frequently with internal stakeholders to gather data or inputs needed for project work Certifications : Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Primary Skills : Python Data Science concepts Pandas, NumPy, Matplotlib Artificial Intelligence Statistical Modeling Machine Learning, Natural Language Processing (NLP), Deep Learning Model Serving Frameworks (e.g., TensorFlow Serving, TorchServe) MLOps(e.g; MLflow, Tensorboard, Kubeflow etc) Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems (Atleast 2) Generative AI, RAG, Finetuning(LoRa, QLoRa) Proficent in any of Cloud Computing Platforms (e.g., AWS, Azure, GCP) Secondary Skills : Expertise in designing scalable and efficient model architectures is crucial for developing robust AI solutions. Ability to assess and forecast the financial requirements of data science projects ensures alignment with budgetary constraints and organizational goals. Strong communication skills are vital for conveying complex technical concepts to both technical and non-technical stakeholders.

We are looking for 8+years experienced candidates for this role. Job Description A minimum of 8 years of professional experience, with at least 6 years in a data science role. Strong knowledge of statistical modeling, machine learning, deep learning and GenAI. Proficiency in Python and hands on experience optimizing code for performance. Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning. Solid understanding of database concepts and experience working with large datasets. Experience deploying and scaling machine learning models in a production environment. Familiarity with machine learning operations (MLOps) and related tools. Good understanding of Generative AI concepts and LLM finetuning. Excellent communication and collaboration skills. Responsibilities include: Lead a high performance team, guide and mentor them on the latest technology landscape, patterns and design standards and prepare them to take on new roles and responsibilities. Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions. Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance. Lead the development and deployment of machine learning/deep learning models to address key business challenges. Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models. Utilize expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems. Design, implement, and optimize data pipelines for model training and deployment. Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe, KServe, or similar). Design and implement APIs for model serving and integration with other systems. Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results. Mentor junior data scientists, providing guidance on technical skills and project execution. Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications. Communicate complex technical concepts and analytical findings to both technical and non-technical audiences. Serves as a primary point of contact for client managers and liaises frequently with internal stakeholders to gather data or inputs needed for project work Certifications : Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Primary Skills : Python Data Science concepts Pandas, NumPy, Matplotlib Artificial Intelligence Statistical Modeling Machine Learning, Natural Language Processing (NLP), Deep Learning Model Serving Frameworks (e.g., TensorFlow Serving, TorchServe) MLOps(e.g; MLflow, Tensorboard, Kubeflow etc) Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems (Atleast 2) Generative AI, RAG, Finetuning(LoRa, QLoRa) Proficent in any of Cloud Computing Platforms (e.g., AWS, Azure, GCP) Secondary Skills : Expertise in designing scalable and efficient model architectures is crucial for developing robust AI solutions. Ability to assess and forecast the financial requirements of data science projects ensures alignment with budgetary constraints and organizational goals. Strong communication skills are vital for conveying complex technical concepts to both technical and non-technical stakeholders.

We are looking for 8+years experienced candidates for this role. Job Description A minimum of 8 years of professional experience, with at least 6 years in a data science role. Strong knowledge of statistical modeling, machine learning, deep learning and GenAI. Proficiency in Python and hands on experience optimizing code for performance. Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning. Solid understanding of database concepts and experience working with large datasets. Experience deploying and scaling machine learning models in a production environment. Familiarity with machine learning operations (MLOps) and related tools. Good understanding of Generative AI concepts and LLM finetuning. Excellent communication and collaboration skills. Responsibilities include: Lead a high performance team, guide and mentor them on the latest technology landscape, patterns and design standards and prepare them to take on new roles and responsibilities. Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions. Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance. Lead the development and deployment of machine learning/deep learning models to address key business challenges. Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models. Utilize expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems. Design, implement, and optimize data pipelines for model training and deployment. Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe, KServe, or similar). Design and implement APIs for model serving and integration with other systems. Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results. Mentor junior data scientists, providing guidance on technical skills and project execution. Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications. Communicate complex technical concepts and analytical findings to both technical and non-technical audiences. Serves as a primary point of contact for client managers and liaises frequently with internal stakeholders to gather data or inputs needed for project work Certifications : Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Primary Skills : Python Data Science concepts Pandas, NumPy, Matplotlib Artificial Intelligence Statistical Modeling Machine Learning, Natural Language Processing (NLP), Deep Learning Model Serving Frameworks (e.g., TensorFlow Serving, TorchServe) MLOps(e.g; MLflow, Tensorboard, Kubeflow etc) Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems (Atleast 2) Generative AI, RAG, Finetuning(LoRa, QLoRa) Proficent in any of Cloud Computing Platforms (e.g., AWS, Azure, GCP) Secondary Skills : Expertise in designing scalable and efficient model architectures is crucial for developing robust AI solutions. Ability to assess and forecast the financial requirements of data science projects ensures alignment with budgetary constraints and organizational goals. Strong communication skills are vital for conveying complex technical concepts to both technical and non-technical stakeholders.

We are looking for 8+years experienced candidates for this role. Job Description A minimum of 8 years of professional experience, with at least 6 years in a data science role. Strong knowledge of statistical modeling, machine learning, deep learning and GenAI. Proficiency in Python and hands on experience optimizing code for performance. Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning. Solid understanding of database concepts and experience working with large datasets. Experience deploying and scaling machine learning models in a production environment. Familiarity with machine learning operations (MLOps) and related tools. Good understanding of Generative AI concepts and LLM finetuning. Excellent communication and collaboration skills. Responsibilities include: Lead a high performance team, guide and mentor them on the latest technology landscape, patterns and design standards and prepare them to take on new roles and responsibilities. Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions. Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance. Lead the development and deployment of machine learning/deep learning models to address key business challenges. Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models. Utilize expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems. Design, implement, and optimize data pipelines for model training and deployment. Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe, KServe, or similar). Design and implement APIs for model serving and integration with other systems. Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results. Mentor junior data scientists, providing guidance on technical skills and project execution. Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications. Communicate complex technical concepts and analytical findings to both technical and non-technical audiences. Serves as a primary point of contact for client managers and liaises frequently with internal stakeholders to gather data or inputs needed for project work Certifications : Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Primary Skills : Python Data Science concepts Pandas, NumPy, Matplotlib Artificial Intelligence Statistical Modeling Machine Learning, Natural Language Processing (NLP), Deep Learning Model Serving Frameworks (e.g., TensorFlow Serving, TorchServe) MLOps(e.g; MLflow, Tensorboard, Kubeflow etc) Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems (Atleast 2) Generative AI, RAG, Finetuning(LoRa, QLoRa) Proficent in any of Cloud Computing Platforms (e.g., AWS, Azure, GCP) Secondary Skills : Expertise in designing scalable and efficient model architectures is crucial for developing robust AI solutions. Ability to assess and forecast the financial requirements of data science projects ensures alignment with budgetary constraints and organizational goals. Strong communication skills are vital for conveying complex technical concepts to both technical and non-technical stakeholders.

We are looking for 8+years experienced candidates for this role. Job Description A minimum of 8 years of professional experience, with at least 6 years in a data science role. Strong knowledge of statistical modeling, machine learning, deep learning and GenAI. Proficiency in Python and hands on experience optimizing code for performance. Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning. Solid understanding of database concepts and experience working with large datasets. Experience deploying and scaling machine learning models in a production environment. Familiarity with machine learning operations (MLOps) and related tools. Good understanding of Generative AI concepts and LLM finetuning. Excellent communication and collaboration skills. Responsibilities include: Lead a high performance team, guide and mentor them on the latest technology landscape, patterns and design standards and prepare them to take on new roles and responsibilities. Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions. Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance. Lead the development and deployment of machine learning/deep learning models to address key business challenges. Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models. Utilize expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems. Design, implement, and optimize data pipelines for model training and deployment. Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe, KServe, or similar). Design and implement APIs for model serving and integration with other systems. Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results. Mentor junior data scientists, providing guidance on technical skills and project execution. Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications. Communicate complex technical concepts and analytical findings to both technical and non-technical audiences. Serves as a primary point of contact for client managers and liaises frequently with internal stakeholders to gather data or inputs needed for project work Certifications : Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Primary Skills : Python Data Science concepts Pandas, NumPy, Matplotlib Artificial Intelligence Statistical Modeling Machine Learning, Natural Language Processing (NLP), Deep Learning Model Serving Frameworks (e.g., TensorFlow Serving, TorchServe) MLOps(e.g; MLflow, Tensorboard, Kubeflow etc) Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems (Atleast 2) Generative AI, RAG, Finetuning(LoRa, QLoRa) Proficent in any of Cloud Computing Platforms (e.g., AWS, Azure, GCP) Secondary Skills : Expertise in designing scalable and efficient model architectures is crucial for developing robust AI solutions. Ability to assess and forecast the financial requirements of data science projects ensures alignment with budgetary constraints and organizational goals. Strong communication skills are vital for conveying complex technical concepts to both technical and non-technical stakeholders.

We are looking for 8+years experienced candidates for this role. Job Description A minimum of 8 years of professional experience, with at least 6 years in a data science role. Strong knowledge of statistical modeling, machine learning, deep learning and GenAI. Proficiency in Python and hands on experience optimizing code for performance. Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning. Solid understanding of database concepts and experience working with large datasets. Experience deploying and scaling machine learning models in a production environment. Familiarity with machine learning operations (MLOps) and related tools. Good understanding of Generative AI concepts and LLM finetuning. Excellent communication and collaboration skills. Responsibilities include: Lead a high performance team, guide and mentor them on the latest technology landscape, patterns and design standards and prepare them to take on new roles and responsibilities. Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions. Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance. Lead the development and deployment of machine learning/deep learning models to address key business challenges. Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models. Utilize expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems. Design, implement, and optimize data pipelines for model training and deployment. Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe, KServe, or similar). Design and implement APIs for model serving and integration with other systems. Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results. Mentor junior data scientists, providing guidance on technical skills and project execution. Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications. Communicate complex technical concepts and analytical findings to both technical and non-technical audiences. Serves as a primary point of contact for client managers and liaises frequently with internal stakeholders to gather data or inputs needed for project work Certifications : Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Primary Skills : Python Data Science concepts Pandas, NumPy, Matplotlib Artificial Intelligence Statistical Modeling Machine Learning, Natural Language Processing (NLP), Deep Learning Model Serving Frameworks (e.g., TensorFlow Serving, TorchServe) MLOps(e.g; MLflow, Tensorboard, Kubeflow etc) Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems (Atleast 2) Generative AI, RAG, Finetuning(LoRa, QLoRa) Proficent in any of Cloud Computing Platforms (e.g., AWS, Azure, GCP) Secondary Skills : Expertise in designing scalable and efficient model architectures is crucial for developing robust AI solutions. Ability to assess and forecast the financial requirements of data science projects ensures alignment with budgetary constraints and organizational goals. Strong communication skills are vital for conveying complex technical concepts to both technical and non-technical stakeholders.

We are looking for 8+years experienced candidates for this role. Job Description A minimum of 8 years of professional experience, with at least 6 years in a data science role. Strong knowledge of statistical modeling, machine learning, deep learning and GenAI. Proficiency in Python and hands on experience optimizing code for performance. Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning. Solid understanding of database concepts and experience working with large datasets. Experience deploying and scaling machine learning models in a production environment. Familiarity with machine learning operations (MLOps) and related tools. Good understanding of Generative AI concepts and LLM finetuning. Excellent communication and collaboration skills. Responsibilities include: Lead a high performance team, guide and mentor them on the latest technology landscape, patterns and design standards and prepare them to take on new roles and responsibilities. Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions. Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance. Lead the development and deployment of machine learning/deep learning models to address key business challenges. Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models. Utilize expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems. Design, implement, and optimize data pipelines for model training and deployment. Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe, KServe, or similar). Design and implement APIs for model serving and integration with other systems. Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results. Mentor junior data scientists, providing guidance on technical skills and project execution. Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications. Communicate complex technical concepts and analytical findings to both technical and non-technical audiences. Serves as a primary point of contact for client managers and liaises frequently with internal stakeholders to gather data or inputs needed for project work Certifications : Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Primary Skills : Python Data Science concepts Pandas, NumPy, Matplotlib Artificial Intelligence Statistical Modeling Machine Learning, Natural Language Processing (NLP), Deep Learning Model Serving Frameworks (e.g., TensorFlow Serving, TorchServe) MLOps(e.g; MLflow, Tensorboard, Kubeflow etc) Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems (Atleast 2) Generative AI, RAG, Finetuning(LoRa, QLoRa) Proficent in any of Cloud Computing Platforms (e.g., AWS, Azure, GCP) Secondary Skills : Expertise in designing scalable and efficient model architectures is crucial for developing robust AI solutions. Ability to assess and forecast the financial requirements of data science projects ensures alignment with budgetary constraints and organizational goals. Strong communication skills are vital for conveying complex technical concepts to both technical and non-technical stakeholders.

We are looking for 8+years experienced candidates for this role. Job Description A minimum of 8 years of professional experience, with at least 6 years in a data science role. Strong knowledge of statistical modeling, machine learning, deep learning and GenAI. Proficiency in Python and hands on experience optimizing code for performance. Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning. Solid understanding of database concepts and experience working with large datasets. Experience deploying and scaling machine learning models in a production environment. Familiarity with machine learning operations (MLOps) and related tools. Good understanding of Generative AI concepts and LLM finetuning. Excellent communication and collaboration skills. Responsibilities include: Lead a high performance team, guide and mentor them on the latest technology landscape, patterns and design standards and prepare them to take on new roles and responsibilities. Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions. Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance. Lead the development and deployment of machine learning/deep learning models to address key business challenges. Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models. Utilize expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems. Design, implement, and optimize data pipelines for model training and deployment. Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe, KServe, or similar). Design and implement APIs for model serving and integration with other systems. Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results. Mentor junior data scientists, providing guidance on technical skills and project execution. Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications. Communicate complex technical concepts and analytical findings to both technical and non-technical audiences. Serves as a primary point of contact for client managers and liaises frequently with internal stakeholders to gather data or inputs needed for project work Certifications : Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Primary Skills : Python Data Science concepts Pandas, NumPy, Matplotlib Artificial Intelligence Statistical Modeling Machine Learning, Natural Language Processing (NLP), Deep Learning Model Serving Frameworks (e.g., TensorFlow Serving, TorchServe) MLOps(e.g; MLflow, Tensorboard, Kubeflow etc) Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems (Atleast 2) Generative AI, RAG, Finetuning(LoRa, QLoRa) Proficent in any of Cloud Computing Platforms (e.g., AWS, Azure, GCP) Secondary Skills : Expertise in designing scalable and efficient model architectures is crucial for developing robust AI solutions. Ability to assess and forecast the financial requirements of data science projects ensures alignment with budgetary constraints and organizational goals. Strong communication skills are vital for conveying complex technical concepts to both technical and non-technical stakeholders.

We are looking for 8+years experienced candidates for this role. Job Description A minimum of 8 years of professional experience, with at least 6 years in a data science role. Strong knowledge of statistical modeling, machine learning, deep learning and GenAI. Proficiency in Python and hands on experience optimizing code for performance. Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning. Solid understanding of database concepts and experience working with large datasets. Experience deploying and scaling machine learning models in a production environment. Familiarity with machine learning operations (MLOps) and related tools. Good understanding of Generative AI concepts and LLM finetuning. Excellent communication and collaboration skills. Responsibilities include: Lead a high performance team, guide and mentor them on the latest technology landscape, patterns and design standards and prepare them to take on new roles and responsibilities. Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions. Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance. Lead the development and deployment of machine learning/deep learning models to address key business challenges. Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models. Utilize expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems. Design, implement, and optimize data pipelines for model training and deployment. Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe, KServe, or similar). Design and implement APIs for model serving and integration with other systems. Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results. Mentor junior data scientists, providing guidance on technical skills and project execution. Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications. Communicate complex technical concepts and analytical findings to both technical and non-technical audiences. Serves as a primary point of contact for client managers and liaises frequently with internal stakeholders to gather data or inputs needed for project work Certifications : Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Primary Skills : Python Data Science concepts Pandas, NumPy, Matplotlib Artificial Intelligence Statistical Modeling Machine Learning, Natural Language Processing (NLP), Deep Learning Model Serving Frameworks (e.g., TensorFlow Serving, TorchServe) MLOps(e.g; MLflow, Tensorboard, Kubeflow etc) Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems (Atleast 2) Generative AI, RAG, Finetuning(LoRa, QLoRa) Proficent in any of Cloud Computing Platforms (e.g., AWS, Azure, GCP) Secondary Skills : Expertise in designing scalable and efficient model architectures is crucial for developing robust AI solutions. Ability to assess and forecast the financial requirements of data science projects ensures alignment with budgetary constraints and organizational goals. Strong communication skills are vital for conveying complex technical concepts to both technical and non-technical stakeholders.