Role Definition: Data Scientists focus on researching and developing AI algorithms and models. They analyse data, build predictive models, and apply machine learning techniques to solve complex problems. Skills: • Proficient: Languages/Framework: Fast API, Azure UI Search API (React) o Databases and ETL: Cosmos DB (API for MongoDB), Data Factory Data Bricks o Proficiency in Python and R o Cloud: Azure Cloud Basics (Azure DevOps) o Gitlab: Gitlab Pipeline o Ansible and REX: Rex Deployment o Data Science: Prompt Engineering + Modern Testing o Data mining and cleaning o ML (Supervised/unsupervised learning) o NLP techniques, knowledge of Deep Learning techniques include RNN, transformers o End-to-end AI solution delivery o AI integration and deployment o AI frameworks (PyTorch) o MLOps frameworks o Model deployment processes o Data pipeline monitoring Expert: (in addition to proficient skills) o Languages/Framework: Azure Open AI o Data Science: Open AI GPT Family of models 4o/4/3, Embeddings + Vector Search o Databases and ETL: Azure Storage Account o Expertise in machine learning algorithms (supervised, unsupervised, reinforcement learning) o Proficiency in deep learning frameworks (TensorFlow, PyTorch) o Strong mathematical foundation (linear algebra, calculus, probability, statistics) o Research methodology and experimental design o Proficiency in data analysis tools (Pandas, NumPy, SQL) o Strong statistical and probabilistic modelling skills o Data visualization skills (Matplotlib, Seaborn, Tableau) o Knowledge of big data technologies (Spark, Hive) o Experience with AI-driven analytics and decision-making systems

Job Summary: We are seeking an experienced and forward-thinking AI Subject Matter Expert (SME) to lead the design, development, and integration of custom and commercial AI solutions. The ideal candidate will bring deep technical expertise, a passion for innovation, and a strategic mindset to drive impactful AI initiatives across the organization. Key Responsibilities: Custom AI Development: Design, build, and deploy tailored AI models and solutions that address specific business challenges and objectives. Commercial AI Integration: Evaluate, recommend, and implement commercial AI platforms and tools to enhance operational efficiency and business outcomes. Research & Innovation: Stay abreast of emerging AI technologies, trends, and research; identify opportunities to innovate and apply cutting-edge techniques. Advanced Data Analysis: Analyze complex datasets to uncover patterns, support model development, and inform decision-making. Cross-functional Collaboration: Partner with data scientists, software engineers, analysts, and business stakeholders to ensure seamless integration of AI solutions. Documentation & Knowledge Sharing: Create thorough documentation of models, algorithms, and development processes; contribute to organizational knowledge bases. Training & Support: Mentor team members and provide training sessions for stakeholders to build AI literacy and adoption across teams. Qualifications Education: Bachelors or Masters degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Experience: 6-9 years of hands-on experience in AI solution development and implementation, with a strong portfolio of successful projects . Technical Proficiency: Expertise in AI/ML frameworks such as TensorFlow, PyTorch, Keras. Strong programming skills in Python (preferred), R, and experience with data tools such as SQL and Pandas. Familiarity with deploying models in production environments . Analytical & Problem-Solving Skills: Ability to interpret complex data, design intelligent systems, and develop actionable insights . Communication: Strong written and verbal communication skills, capable of translating complex concepts for non-technical audiences . Project Management: Demonstrated ability to lead AI projects end-to-end from planning to deployment and performance monitoring . Adaptability: Comfortable working in a dynamic, fast-paced environment with evolving business needs.

Role & responsibilities Develop, improve, and expand deployment pipeline infrastructure and services Build and deploy hyperscale machine learning pipelines into production Works closely with the developer teams to provide a self-service test integration, test coverage and quality Collaborate with the Security Engineering and Architecture teams to ensure code meets security requirements and is deployed in alignment with standards and governance processes Debug and resolve the issues developers encounter when attempting to use cloud services, with an eye toward continuously improving developer experience Execute against a vision of continuous integration and delivery that empowers developers Preferred candidate profile Building Infrastructure-as-code for Public Cloud using Terraform Experience in a Dev Ops engineering or equivalent role. Experience developing, enhancing, and maintaining CI/CD automation and configuration management using tools such as Jenkins, Snyk, and GitHub Experience with MLOps and orchestration tools such as Airflow, Kubeflow, Optuna, Mlflow or other similar MLOps tools. Experience with operationalizing and migrating ML models into production at scale Experience developing large scale model inference solutions using a parallel execution framework using Spark, EMR, or Databricks Experience developing complex orchestration and MLOps pipelines stitching together large volumes of data for training and scoring Proficiency with Apache Spark, EMR/DataProc and Cloud based tools (Snowflake, Redshift, EMR, Glue, Step Functions, Lambda, Step functions, AWS Batch, or similar etc.) Experience with ML libraries like H20, scikit learn and deep learning frameworks (PyTorch, TensorFlow, etc.) Familiarity with large scale production systems and technologies; for example load balancing, monitoring, distributed systems, and configuration management Development skills with at least one of the following programming languages: Python (preferred), Node.js, Bash. Knowledge of testing automation and code scanning tools Knowledge and experience with running distributed systems in public cloud environments such as AWS, Azure, and GCP. Knowledge of container orchestration platforms such as Kubernetes Knowledge of CDNs, DNS, web servers, and other supporting Internet services Source code management tools using Git. Experienced with standard Gitflow workflow Familiar with best practices for infrastructure security, reliability, and fault tolerance Passionate about automating and improving processes

Role & responsibilities Urgent Hiring for one of the reputed MNC Immediate joiners Only Females Bang / Pune / Hyd Exp - 4 - 12 Years Job Description: Senior Data Scientist - Multi-Agent AI Systems About the Role We are seeking an exceptional Data Scientist with specialized expertise in developing multi-agent AI systems. In this role, you will design, implement, and optimize complex AI ecosystems where multiple intelligent agents collaborate to solve sophisticated problems. You will leverage your deep understanding of generative AI, retrieval-augmented generation (RAG), and prompt engineering to create cutting-edge solutions that push the boundaries of artificial intelligence. Key Responsibilities Design and develop generative AI-based multi-agent systems that can collaborate, communicate, and coordinate to achieve complex objectives Architect and implement RAG-based chatbot solutions that effectively leverage knowledge bases and external data sources Create sophisticated prompt engineering strategies to optimize AI agent behavior and inter-agent communication Build, train, and fine-tune generative AI models for various applications within multi-agent systems Develop robust evaluation frameworks to measure and improve multi-agent system performance Implement efficient knowledge sharing mechanisms between AI agents Write clean, efficient, and well-documented Python code for production-ready AI systems Collaborate with cross-functional teams to integrate multi-agent systems into broader product ecosystems Stay at the forefront of AI research and incorporate state-of-the-art techniques into our solutions Required Qualifications Master's or PhD in Computer Science, Machine Learning, Artificial Intelligence, or related field 4+ years of professional experience in data science or machine learning engineering Extensive experience with Python programming and related data science/ML libraries Demonstrated expertise in developing and deploying generative AI models (e.g., LLMs, diffusion models) Proven experience building RAG-based systems and implementing vector databases Strong background in prompt engineering for large language models Experience designing and implementing generative AI-based multi-agent architectures Excellent problem-solving skills and ability to optimize complex AI systems Preferred candidate profile Experience with LangChain, AutoGPT, CrewAI, or similar frameworks for building agent-based systems Familiarity with orchestration tools for managing complex AI workflows Knowledge of agent communication protocols and collaborative problem-solving frameworks Experience with distributed systems and cloud computing platforms (AWS, GCP, Azure) Contributions to open-source AI projects or research publications in relevant fields Experience with knowledge graphs and semantic reasoning systems Familiarity with MLOps practices and deployment of AI systems at scale

Key Responsibilities : Design, develop, and deploy machine learning models and AI solutions that address complex business challenges, particularly within the AEC domain. Build scalable, production-grade AI/ML pipelines, including data preprocessing, feature engineering, model training, validation, deployment, and monitoring. Collaborate with Data Engineers, Data Scientists, and other stakeholders to design and implement end-to-end AI/ML solutions. Ensure that deployed models are reliable, maintainable, and scalable while optimizing their performance in production environments. Develop APIs or microservices for integrating AI/ML models into business solutions. Optimize AI/ML workflows by implementing MLOps practices, including CI/CD pipelines, model retraining, and version control. Apply state-of-the-art algorithms and techniques such as deep learning, NLP, computer vision, and time-series analysis for domain-specific use cases. Maintain adherence to data privacy, security, and ethical AI standards throughout the lifecycle of AI/ML models. Stay abreast of the latest developments and advancements, including new and emerging technologies & best practices and new tools & software applications and how they could impact CDM Smith. Assist with the development of documentation, standards, best practices, and workflows for data technology hardware/software in use across the business. Performs other duties as required Skills and Abilities: Strong expertise in building and deploying machine learning models using frameworks such as TensorFlow, PyTorch, or Scikit-learn. Hands-on experience with cloud-based AI/ML services, particularly in Microsoft Azure and Databricks. Proficiency in programming languages such as Python or R, with a strong understanding of libraries and tools for data science and machine learning. Experience with MLOps practices, including automated pipelines, model versioning, monitoring, and lifecycle management. Familiarity with distributed computing frameworks like Apache Spark and scalable AI/ML training techniques. Expertise in domain-specific AI techniques such as Natural Language Processing (NLP), computer vision, and predictive analytics. Knowledge of data privacy, security, and ethical AI principles, ensuring compliance with relevant standards. Excellent problem-solving and critical thinking skills to identify and address technical challenges effectively. Excellent interpersonal and presentation skills to build strategic relationships with colleagues, stakeholders, and partners. Strong critical thinking skills to generate innovative solutions and improve business processes. Ability to effectively communicate complex technical concepts to both technical and nontechnical audiences. Detail oriented with the ability to assist with executing highly complex or specialized projects. Minimum Qualifications: Bachelors degree. 6 years of related experience. Equivalent additional related experience will be considered in lieu of a degree.

Job Description : Strong understating of Python, ML concepts and frameworks, Fast API, Graph QL Experience in developing scalable APIs. Knowledge of AWS, preferred services are storage, EC2, Kubernetes Exposure of ML best practices, documentation and unit testing. ML Flow, AirFlow, ML pipeline creation, drift monitoring and control Experience in developing and deploying machine learning models in a production environment using CI/CD. Communicate with clients to understand requirements and ask right questions. Knowledge of Django and database design will be added advantage. Strong analytical and problem-solving skills. Experience in proprietary and public LLMs, including finetuning, inference optimization Experience in latest LLM frameworks/libraries (like LangChain, Langfuse, Ragas, Llamaindex, Huggingface, Chainlit)

Title Python & AWS Full Stack Engineer Location - Bangalore Exp. Range 1 Lead (8-10 years) & 1 Sr (5-6 Years) Mode - Hybrid (3 days / week WFO) Shift Timing 7 AM to 4 PM. Must Have Skills : Good experience of Django, Python for building Application Development & packaging (docker & containerization) Good experience of AWS services - EC2, ECR, CloudWatch. Hands on experience of : MLOps (AI Model deployment) and related activities. Good to Have : Good understanding of GenAI & AI. Exposure to Fraud & cyber security in BFS. Roles and Responsibilities: Develop and maintain full-stack web applications using Python and AWS. Collaborate with the product and design teams to define software requirements and specifications. Design,implement, and maintain back-end and front-end component applications. Write clean, efficient, and maintainable code while ensuring optimal system performance. Perform code reviews and mentor junior developers to maintain code quality. Participate in architectural and design discussions to contribute to the technical vision of the projects. Troubleshoot, debug, and resolve software defects and issues. Stay up to date with the latest industry trends, best practices, and emerging technologies. Contribute to the continuous improvement of development processes and methodologies.

Experience: 4+ years of hands-on experience in deploying and monitoring machine learning models. Basic understanding of ML and data science/modeling aspects. Experience with open-source tools for MLOps, such as Kubernetes, Docker, and Apache Airflow and developing and streamlining workflows, building scalable pipelines, etc. Ensuring successful model development, testing, optimization, scaling, monitoring/observability, and governance Continuous integration and continuous deployment (CI/CD) Proficiency in cloud platforms, especially Azure and/or AWS. Strong scripting skills (e.g., Python) for automation and tool development. Problem-Solving: o Strong problem-solving skills with a focus on finding efficient and scalable solutions. o Ability to learn new tools on the go and developing best practices is mandatory.

Role & responsibilities Architect and lead the development of machine learning solutions, aligning with business objectives and strategic initiatives. Oversee a team of data scientists and engineers, providing technical guidance and mentorship. Develop and implement advanced machine learning models, including deep learning, time series forecasting, and natural language processing. Collaborate with cross-functional teams to identify opportunities for ML-driven solutions and translate business requirements into technical specifications. Build and maintain robust ML pipelines, incorporating data ingestion, preprocessing, feature engineering, model training, evaluation, deployment, and monitoring. Leverage cloud platforms (e.g., Azure) for efficient ML model development, training, and deployment. Drive the adoption of MLOps practices to improve model lifecycle management and operational efficiency. Utilize data visualization tools (Superset, Power BI) to communicate insights and drive data-informed decision making. Experience in developing Analytics, Machine learning offerings end to end including training models, serving models using standard frameworks like TF/TFX/Pytorch/Azure AIML, deployments at scale Embeddings and Vector Space Model Expertise: Good knowledge of embedding techniques and vector space models as they apply to generative AI. This includes creating, tuning, and leveraging embeddings for various types of data (text, images, etc.). Large Language Model Proficiency: Experience in working with LLMs, particularly in their application to embeddings and vector space models. Ability to fine-tune these models for specific applications. Preferred candidate profile Advanced degree in Computer Science, Statistics, or a related field. 10-12+ years of experience in machine learning and data science. Proven experience in architecting and implementing large-scale ML systems. Strong proficiency in Python and related ML libraries (TensorFlow, PyTorch, Scikit-learn). Expertise in data engineering, including data extraction, transformation, and loading (ETL) processes. Experience with cloud platforms (Azure preferred), big data technologies, and containerization. Demonstrated ability to lead and mentor data science teams. Excellent communication and interpersonal skills.

JOB DESCRIPTION Job Title: AI Engineer Experience: 5-10 years Location: Pune, India Role Definition: This is a specialized role for an AI Software Engineer responsible for designing, building, and deploying scalable AI models and systems. You will work with machine learning frameworks, cloud platforms, and data engineering tools to create and optimize AI solutions. Key Responsibilities: Design, develop, and deploy AI models and systems. Work with machine learning frameworks and implement AI/ML algorithms. Manage data pipelines and monitor data flows. Implement and optimize MLOps frameworks. Collaborate with cross-functional teams to integrate AI solutions with cloud platforms. Required Skills: Proficient: Languages/Frameworks: Fast API, Azure UI Search API (React) Cloud: Azure Cloud Basics (Azure DevOps) Version Control & CI/CD: Gitlab Pipeline Deployment: Ansible and REX (Rex Deployment) Data Science: Prompt Engineering and Modern Testing Data Pipeline: Development and Monitoring AI/ML Knowledge: Understanding of AI/ML algorithms and their applications MLOps: Familiarity with MLOps frameworks Cloud Platforms: Knowledge of Azure ML Model Deployment: Experience in model deployment processes Expert (in addition to the proficient skills): Languages/Frameworks: Expertise in Azure Open AI Data Science: Proficient with Open AI GPT family of models (4o/4/3), Embeddings, and Vector Search Databases and ETL: Experience with Azure Storage Account, PostgreSQL, and Cosmos ML Frameworks: Hands-on experience with TensorFlow, PyTorch, and Scikit-learn Cloud Platforms: In-depth knowledge of AWS SageMaker and Google AI Platform Data Engineering: Expertise in data preprocessing, feature engineering, and model evaluation Software Engineering: Strong understanding of version control, CI/CD, containerization, and overall software engineering principles Big Data & Distributed Computing: Familiarity with tools such as Spark and Hadoop Performance Optimization: Ability to optimize models for performance and scalability Search Technologies: Experience with Azure AI Search

Responsibilities: Design and implement cloud solutions using AWS and Azure. Develop and maintain Infrastructure as Code (IAC) with Terraform. Create and manage CI/CD pipelines using GitHub Actions and Azure DevOps. Automate deployment processes and provisioning of compute instances and storage. Orchestrate container deployments with Kubernetes. Develop automation scripts in Python, PowerShell, and Bash. Monitor and optimize cloud resources for performance and cost-efficiency using tools like Datadog and Splunk. Configure Security Groups, IAM policies, and roles in AWS\Azure. Troubleshoot production issues and ensure system reliability. Collaborate with development teams to integrate DevOps and MLOps practices. Create comprehensive documentation and provide technical guidance. Continuously evaluate and integrate new AWS services and technologies Cloud engineering certifications (AWS, Terraform) are a plus. Excellent communication and problem-solving skills. Minimum Qualifications: Bachelors Degree in Computer Science or equivalent experience. Minimum of five years in cloud engineering, DevOps, or Site Reliability Engineering (SRE). Hands-on experience with AWS and Azure cloud services, including IAM, Compute, Storage, ELB, RDS, VPC, TGW, Route 53, ACM, Serverless computing, Containerization, CloudWatch, CloudTrail, SQS, and SNS. Experience with configuration management tools like Ansible, Chef, or Puppet. Proficiency in Infrastructure as Code (IAC) using Terraform. Strong background in CI/CD pipelines using GitHub Actions and Azure DevOps. Knowledge of MLOps or LLMops practices. Proficient in scripting languages: Python, PowerShell, Bash. Ability to work collaboratively in a fast-paced environment. Preferred Qualifications: Advanced degree in a technical field. Extensive experience with ReactJS and modern web technologies. Proven leadership in agile and project management. Advanced knowledge of CI/CD and industry best practices in software development.

Aegan Technologies is looking for an experienced MLOps Engineer/Lead Job Title: MLOps Engineer/Lead Company: Aegan Technologies Location: Mumbai, Bangalore, Pune, Chennai (Hybrid) Notice Period: 0 to 30 days only Job Description: Key Responsibilities: Architect and deploy ML solutions in AWS. Select appropriate ML technologies and AWS services for implementation. Build and maintain infrastructure for ML model development, deployment, and monitoring. Implement CI/CD pipelines to streamline development and deployment. Collaborate with data scientists on model serving, versioning, and reproducibility. Optimize model performance in production and proactively resolve issues. Automate workflows to improve efficiency and reduce manual errors. Document best practices and provide training on MLOps methodologies. Stay updated on the latest MLOps tools, technologies, and trends. Qualifications: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 3+ years of experience in MLOps, DevOps, or related fields. Strong programming skills in Python, GoLang (Java, C++, Scala is a plus). Experience with ML frameworks (TensorFlow, PyTorch, scikit-learn). Proficiency with CI/CD tools (GitHub Actions). Hands-on experience with AWS services for ML. Expertise in containerization & orchestration (Docker, Kubernetes). Knowledge of Infrastructure-as-Code tools (AWS CDK, CloudFormation). Strong understanding of the ML lifecycle , from data preprocessing to deployment. Excellent problem-solving and communication skills. Preferred Qualifications: AWS Certified Machine Learning Specialty certification. Experience with feature stores, model registries, ML monitoring tools (MLflow, Tecton, Seldon). Familiarity with data engineering tools (AWS EMR, Glue, Apache Spark). Knowledge of ML security best practices . Experience with A/B testing and model performance monitoring . If you're interested, send your updated resume to raj@aegan-global.com or contact 8438411241

Job Summary As a Senior Machine Learning Solutions Architect at NetApp, you will be at the forefront of driving innovation by designing and implementing cutting-edge Generative AI (GenAI) and Machine Learning (ML) solutions. Your work will empower our internal sales teams, partners, and customers with actionable insights derived from advanced analytics. You will lead the design of scalable, robust, and production-ready AI/ML systems, from real-time predictive analytics to LLM-driven automation, ensuring seamless integration into our existing infrastructure. Collaborating with cross-functional teams, including senior software developers, technical directors, and business stakeholders, you will play a pivotal role in shaping the future of data-driven decision-making at NetApp. This role demands a creative, results-driven, and collaborative individual who thrives in a fast-paced environment and is passionate about leveraging AI/ML to solve complex business challenges. Job Requirements Architect and Deploy AI/ML Solutions. Design, develop, and implement scalable and robust ML systems using techniques such as classical machine learning, Generative AI models (e.g., LLMs), and AI agents. Ensure the reliability, scalability, and performance of AI/ML models in production environments. Oversee ML design reviews and establish best practices for end-to-end ML systems, including MLOps and CI/CD pipelines. Collaborate Across Teams. Partner with data engineers to build scalable data pipelines for AI/ML-driven solutions, including curated data pipelines, ML feature pipelines, and deployment services. Work closely with business stakeholders to identify and unlock new use cases, driving innovation through data intelligence. Participate in cross-functional meetings, workshops, and planning sessions to align data engineering and AI/ML initiatives with organizational goals. Technical Leadership and Mentorship. Act as a technical thought leader in the AI/ML space, providing guidance on data governance, model lifecycle management, and MLOps practices. Mentor and coach data scientists and cross-functional team members, fostering a culture of innovation, collaboration, and continuous learning. Industry Representation. Represent NetApp as a leader and ambassador in the AI/ML community, building relationships with external partners and promoting the companys capabilities at industry and academic conferences. Education Masters or Bachelors degree in Computer Science, Engineering, Applied Mathematics, Statistics, Data Science, or a related field. 10+ years of experience as a Data and Machine Learning Engineer, with a proven track record of architecting, designing, and implementing end-to-end ML solutions, building scalable data/feature pipelines, and shipping AI/ML-driven products at scale. Recent experience with experimenting and deploying Large Language Models (LLMs) to production is a strong.

Experience in leading a team as well as handling customer stakeholders Hands on experience in building scalable solutions using microservices & deployment of ML Algorithms in production Excellent programming skills in Python, a good understanding in Go is a plus. Experience in Test driven Development Expert knowledge of building and maintaining in Production Experience in cloud - AWS/Azure/Google is mandatory Frontend Exposure to Front end frameworks like React/Angular having led teams and built applications Experience is creating reusable components and libraries Experience is optimising the performance of the web applications Exposure to mobile application development is a plus Database Proficient with SQL, RDBMS such as Postgres, MySQL, SQL Server, Oracle and/or experience with NoSQL DBMSs such as Mongo DB Familiarity with some ORM (Object Relational Mapper) libraries like SQLAlchemy Experience in optimising the performance of the queries. Backend Experience in server-side/back-end full cycle product development in a production environment in Python Hands on experience in python server frameworks like Django/Flask/FastAPI Strong testing and debugging skills. Strong trouble-shooting skills that span systems (Linux), network, and application Understanding of the threading limitations of Python, and multi-process architecture. CI/CD Good knowledge of software engineering practices like version control (GIT) and Dev ops Understanding of ML/AI Pipeline & Development life cycle & tools, MLOps Knowledge

About the role : We are looking for a Machine Learning Engineer who will work on a broad range of cutting-edge data analytics and machine learning problems across a variety of industries. More specifically, you will Collaborate with data scientists to transition machine learning models from development to production. Design and implement MLOps pipelines for model training, deployment, and monitoring on GCP. Utilize GCP services for version control, continuous integration, and continuous deployment (CI/CD) of machine learning models. Implement monitoring and logging solutions to track the performance and health of deployed models. Optimize and scale machine learning workflows on GCP to handle production workloads efficiently. Stay updated on the latest developments in MLOps practices and GCP services to ensure best practices are followed. Desired Skills and Experience : 3+ years of experience with at least 3+ years of relevant DS experience. Good working knowledge on GCP Proficient in a structured Python Follows good software engineering practices and has an interest in building reliable and robust software. Good knowledge of DS concepts and professional experience in developing and enhancing algorithms and models to solve business problem Conducting quantitative analyses and interpreting results Working knowledge of Linux or Unix environments ideally in a cloud environment. Working knowledge of Spark/PySpark is desirable. Excellent written and verbal communication skills. B.Tech from Tier-1 college M.S or M. Tech is preferred.

3+ years experience as a software engineer or equivalent designing large data-heavy distributed systems and/or high-traffic web-apps Experience in at least one programming language (Python-2 yrs strong coding is must) or java. Hands-on experience designing & managing large data models, writing performant SQL queries, and working with large datasets and related technologies Experience designing & interacting with APIs (REST/GraphQL) Experience working with cloud platforms such as GCP, Big Query Experience in DevOps processes/tooling (CI/CD, GitHub Actions), using version control systems (Git strongly preferred), and working in a remote software development environment Strong analytical, problem solving and interpersonal skills, have a hunger to learn, and the ability to operate in a self-guided manner in a fast-paced rapidly changing environment Preferred: Experience using infrastructure as code frameworks (Terraform) Preferred: Experience using big data tools such as Spark/PySpark Preferred: Experience using or deploying MLOps systems/tooling (eg. MLFlow) Must have: Experience in pipeline orchestration (eg. Airflow) Must Have Experience in Data Flow 1 yr experience atleast Preferred: Experience using infrastructure as code frameworks (Terraform) Preferred: Experience in an additional programming language (JavaScript, Java, etc) Preferred: Experience using data science/machine learning technologies.

Skills : C++, Python, ROS, ROS2, ROS Navigation stack & path planningOS : Windows, Linux, Ubuntu Other Skills : Configuration Management, Software Testing, Basics of QT, Basics of image processing, Object Detection and Basics of machine learning.Job details: Proficiency in robotics middleware such as ROS (Robot Operating System) Development of customized Nodes in ROS according to Robotic/Sensor Application Familiarity with Linux. Ability to write robust code in python and C++ Knowledge of math, probability and algorithms Familiarity with machine learning frameworks and libraries. Experience developing software systems for localization & mapping. Object detection & tracking and control of the robot. Ability to select hardware to run ML model with required latency. Experience with Integrating Robotic Sub Systems, Experience working on Unmanned Ground Vehicles and Unmanned Aerial Vehicles. Troubleshooting robotic systems and applications. Proven experience as an Artificial Intelligence /Machine Learning developer

About the role : We are looking for a Machine Learning Engineer who will work on a broad range of cutting-edge data analytics and machine learning problems across a variety of industries. More specifically, you will Collaborate with data scientists to transition machine learning models from development to production. Design and implement MLOps pipelines for model training, deployment, and monitoring on GCP. Utilize GCP services for version control, continuous integration, and continuous deployment (CI/CD) of machine learning models. Implement monitoring and logging solutions to track the performance and health of deployed models. Optimize and scale machine learning workflows on GCP to handle production workloads efficiently. Stay updated on the latest developments in MLOps practices and GCP services to ensure best practices are followed. Desired Skills and Experience : 3+ years of experience with at least 3+ years of relevant DS experience. Good working knowledge on GCP Proficient in a structured Python Follows good software engineering practices and has an interest in building reliable and robust software. Good knowledge of DS concepts and professional experience in developing and enhancing algorithms and models to solve business problem Conducting quantitative analyses and interpreting results Working knowledge of Linux or Unix environments ideally in a cloud environment. Working knowledge of Spark/PySpark is desirable. Excellent written and verbal communication skills. B.Tech from Tier-1 college M.S or M. Tech is preferred.

Overview In this role, we are seeking an Associate Manager Offshore Program & Delivery Management to oversee program execution, governance, and service delivery across DataOps, BIOps, AIOps, MLOps, Data IntegrationOps, SRE, and Value Delivery programs. This role requires expertise in offshore execution, cost optimization, automation strategies, and cross-functional collaboration to enhance operational excellence. Manage and support DataOps programs, ensuring alignment with business objectives, data governance standards, and enterprise data strategy. Assist in real-time monitoring, automated alerting, and self-healing mechanisms to improve system reliability and performance. Contribute to the development and enforcement of governance models and operational frameworks to streamline service delivery and execution roadmaps. Support the standardization and automation of pipeline workflows, report generation, and dashboard refreshes to enhance efficiency. Collaborate with global teams to support Data & Analytics transformation efforts and ensure sustainable, scalable, and cost-effective operations. Assist in proactive issue identification and self-healing automation, enhancing the sustainment capabilities of the PepsiCo Data Estate. Responsibilities Support DataOps and SRE operations, assisting in offshore delivery of DataOps, BIOps, Data IntegrationOps, and related initiatives. Assist in implementing governance frameworks, tracking KPIs, and ensuring adherence to operational SLAs. Contribute to process standardization and automation efforts, improving service efficiency and scalability. Collaborate with onshore teams and business stakeholders, ensuring alignment of offshore activities with business needs. Monitor and optimize resource utilization, leveraging automation and analytics to improve productivity. Support continuous improvement efforts, identifying operational risks and ensuring compliance with security and governance policies. Assist in managing day-to-day DataOps activities, including incident resolution, SLA adherence, and stakeholder engagement. Participate in Agile work intake and management processes, contributing to strategic execution within data platform teams. Provide operational support for cloud infrastructure and data services, ensuring high availability and performance. Document and enhance operational policies and crisis management functions, supporting rapid incident response. Promote a customer-centric approach, ensuring high service quality and proactive issue resolution. Assist in team development efforts, fostering a collaborative and agile work environment. Adapt to changing priorities, supporting teams in maintaining focus on key deliverables. Qualifications 6+ years of technology experience in a global organization, preferably in the CPG industry. 4+ years of experience in Data & Analytics, with a foundational understanding of data engineering, data management, and operations. 3+ years of cross-functional IT experience, working with diverse teams and stakeholders. 12 years of leadership or coordination experience, supporting team operations and service delivery. Strong communication and collaboration skills, with the ability to convey technical concepts to non-technical audiences. Customer-focused mindset, ensuring high-quality service and responsiveness to business needs. Experience in supporting technical operations for enterprise data platforms, preferably in a Microsoft Azure environment. Basic understanding of Site Reliability Engineering (SRE) practices, including incident response, monitoring, and automation. Ability to drive operational stability, supporting proactive issue resolution and performance optimization. Strong analytical and problem-solving skills, with a continuous improvement mindset. Experience working in large-scale, data-driven environments, ensuring smooth operations of business-critical solutions. Ability to support governance and compliance initiatives, ensuring adherence to data standards and best practices. Familiarity with data acquisition, cataloging, and data management tools. Strong organizational skills, with the ability to manage multiple priorities effectively.

Hi, Wishes from GSN!!! Pleasure connecting with you!!! We been into Corporate Search Services for Identifying & Bringing in Stellar Talented Professionals for our reputed IT / Non-IT clients in India. We have been successfully providing results to various potential needs of our clients for the last 20 years. At present, GSN is hiring MLOPS ENGINEER for one of our leading MNC client. PFB the details for your better understanding: 1. WORK LOCATION : Bangalore & Mumbai 2. Job Role: MLOPS ENGINEER 3. EXPERIENCE : 5+ yrs 4. CTC Range: Rs. 20 LPA to Rs. 30 LPA 5. Work Type : WFO (all 5 days) ****** Looking for IMMEDIATE JOINER ****** Who are we looking for ? MLOPS Engineer with AWS Experience. Required Skills : Set up and manage infrastructure for model inferencing across our AWS DEV, TEST, and PROD environments on AWS using services like EC2, ECS, EKS, Lambda, SageMaker, Step Functions, Networking etc. and expertise in IAC tools like Terraform. Should have prior experience in deploying ML models on any MLOps platforms (AWS, GCP, Azure, etc.) Having prior experience working on enterprise scale applications and environments. Develop highly scalable architectures both server and serverless. Familiarity with frameworks like Docker, Django, FastAPI Work closely with Data Scientists to deploy their code , necessitating a strong grasp of Python, prompt engineering (including but not limited to Lang Chain and Lang Graph), data science concepts, as well as foundational mathematics and statistics. ****** Looking for IMMEDIATE JOINER ****** Feel free to CALL me @ 8939 666 294 for IMMEDIATE response . Best regards, Shobana | GSN | shobana@gsnhr.net | 8939666294 | Google review : https://g.co/kgs/UAsF9W

Architecture & Design: Design scalable, reliable, and secure AI/ML platforms; define technical specifications for AI applications. Core ML Use Cases Computer Vision & NLP Mlops & Deployment Explainable AI (XAI)

Technical Lead (Enterprise AI Systems) We are seeking a Technical Lead to drive the design and development of our enterprise-grade AI product. This role requires a deep understanding of AI/ML systems, cloud-native architectures, and scalable software design. The applicant should have mandatorily worked on enterprise grade AI product or project for at least 2 - 3 years. This role includes at least 50% hands on development, 30% LLD and the remaining 20% of HLD / Architecture. The role is on the technical career path and is NOT suitable for project managers, technical managers, engineering managers etc. Key Responsibilities: Develop scalable, modular, and secure AI-driven software systems. Design APIs, microservices, and cloud infrastructure for AI workloads. Ensure seamless AI model integration and efficient scaling. Optimize performance & scalability of systems. Implement caching, load balancing, and distributed computing. Ensure security compliance (GDPR, SOC2) and secure data handling. Enforce best practices in authentication, encryption, and API security. Mandatory Skills & Qualifications: Experience: 8 - 12 years in software architecture & design, with at least 3+ years focused on AI/ML product design & development. Last 1 - 2 years should include deep Gen AI experience in enterprise grade AI projects or applications. Experience with design and developing applications with LLMs, RAG Applications, Gen AI Agents. Fine-tuning open-source LLMs. Experience with deploying and monitoring LLMs. Experience with LLM evaluation frameworks or tools like DeepEval, Langfuse or similar. Experience with frameworks like langchain, llamaindex, pydantic etc. Proficiency in RDBMS like PostgreSQL and knowledge of vector databases (eg: chromadb,Pinecone, FAISS, Weaviate). Strong background in microservices, RESTful & GraphQL APIs, event-driven architectures, Working experience with Kafka, RabbitMQ etc... Proficient in Python, Java for backend development Experience with TensorFlow, PyTorch, Hugging Face etc... Skilled in data processing (NumPy, Pandas, Dask,NLTK) and front-end (TypeScript, React.js). Experience in scalable AI systems, data pipelines, MLOps, and real-time inference. Strong understanding of security protocols, IAM, OAuth2, JWT, RBAC. Expertise in cloud platforms (AWS, GCP, Azure), Kubernetes, Docker. Hands-on experience with CI/CD (Eg: github,Jenkins etc...) Hands-on with system monitoring (Eg: ELK Stack,OpenTelemetry, Prometheus, Grafana). Nice-to-Have Skills: Ensuring AI model interpretability, fairness, and bias mitigation strategies. Optimizing model inference using quantization, distillation, and pruning techniques. Experience in deploying AI models in production at scale, including MLOps best practices. Designing AI systems with privacy-preserving techniques (differential privacy, homomorphic encryption etc.). Experience with knowledge graph-based AI applications.

Primary skills- GCP, Python CODING MUST, SQL Coding skills, Big Query, Dataflow, Airflow, Kafka and Airflow Dags. Bachelors Degree or equivalent experience in Computer Science or related field Required- Immediate or 15 days Job Description 3+ years experience as a software engineer or equivalent designing large data-heavy distributed systems and/or high-traffic web-apps Experience in at least one programming language (Python-2 yrs strong coding is must) or java. Hands-on experience designing & managing large data models, writing performant SQL queries, and working with large datasets and related technologies Experience designing & interacting with APIs (REST/GraphQL) Experience working with cloud platforms such as GCP, Big Query Experience in DevOps processes/tooling (CI/CD, GitHub Actions), using version control systems (Git strongly preferred), and working in a remote software development environment Strong analytical, problem solving and interpersonal skills, have a hunger to learn, and the ability to operate in a self-guided manner in a fast-paced rapidly changing environment Preferred: Experience using infrastructure as code frameworks (Terraform) Preferred: Experience using big data tools such as Spark/PySpark Preferred: Experience using or deploying MLOps systems/tooling (eg. MLFlow) Must have: Experience in pipeline orchestration (eg. Airflow) Must Have Experience in Data Flow 1 yr experience atleast Preferred: Experience using infrastructure as code frameworks (Terraform) Preferred: Experience in an additional programming language (JavaScript, Java, etc) Preferred: Experience using data science/machine learning technologies

Designation: Senior Software Engineer IExperience: 6-8 years Our Engineers are at the forefront of how customers interact with C2FO. Working with our Product team, we create applications for internal and external customers. Our philosophy: short iterations with a focus on scalability while ensuring maintainability. Essential Requirements Concerned with the success of their teammates as well as themselves. Respectful towards teammates regardless of their abilities. Collaborate with C2FO's Engineering, Product, DevOps, and operational stakeholders to define and refine requirements, break down work into smaller tasks, and provide accurate estimations. Work with minimal help on big slices of work/requirements that require high level and low level design and external dependencies while tracking and pushing the work through the process reliably. Take ownership of the codebase and process and contribute to any required improvements. Willing to debate, obtain, and move forward with the best solution. Always digging deeper to understand the problem space and the 'why' of your work. Vigilant in identifying tech debt and always improving how we do things. Persistent in facing roadblocks; dispatches them efficiently, pulling in others as necessary. Proficient communication in English, both written and verbal Comfortable with source control, especially GitHub. Engage in contributing to improve processes, take technical decisions, improve technical systems to steer the team towards the team and C2FO goals. Solicit feedback from peers, teammates, and managers to identify improvement areas and take steps to learn and grow. Passionate about testing, code quality, and continuous integration. Uphold our high engineering standards and consistently deliver quality code & documentation. Mentor and guide other engineers, creating a collaborative learning and working environment. Suggest, Share, plan for, and implement new tools or processes to help the team or engineering be more collaborative, effective, or efficient. Improve our documentation and document design decisions and rationale. Take part in the on-call rotations. Strong problem-solving skills. Basic Qualifications Minimum Six years of experience as a Software Engineer in developing products Bachelor's or higher degree or its equivalent Necessary Experience on: Backend programming languages such as Java, Python, or GoLang Relational database (PostgreSQL) RESTful or GraphQL APIs AWS or GCP or other Cloud Work experience in a microservice & event-driven architectures and messaging queues (e.g., RabbitMQ/Kafka/SNS) Work experience in building scalable, fault-tolerant, durable, highly available distributed Systems Strong understanding of System Design & Architecture Experience on building frontend to backend flow/feature Preferred Experience on : Nest.js/ Node.Js GoLang ORM tools Docker and Kubernetes Datadog or other similar tools

DS Key Responsibilities Combine expertise in mathematics statistics computer science and domain knowledge to create AIML models to solve various business challenges Collaborate closely with the AI Technical Manager and GCC Petro technical professionals and data engineers to integrate models into the business framework Identify and frame opportunities to apply advanced analytics modeling and related technologies to data to help businesses gain insight and improve decision making workflow and automation Understand and communicate the value of proposed opportunity with team members and other stakeholders Identify needed data and appropriate technology to solve identified business challenges Clean data and develop and test models Establish the life cycle management process for models Provide technical mentoring in modeling and analytics technologies the specifics of the modeling process and general consulting skills Drive innovation in AIML to enhance capabilities in data driven decision making Aligns with team on shared goals and outcomes recognizes others contributions and work collaboratively seek diverse perspectives Takes actions to develop self and others beyond existing skillset Encourages innovative ideas adapts to change and changing technologies Understand and communicate data insights and model behaviors to stakeholders with varying levels of technical expertise Required Qualification Minimum 5 years of experience in designing and developing AIML models and or various optimization algorithms 5 to 9 years of experience Solid foundation in mathematics probability and statistics with demonstrated depth of knowledge and experience in advanced analytics and data science methodologies eg supervised and unsupervised learning statistics data science model development Proficiency in Python and working knowledge of cloud AIML services Azure Machine Learning and Databricks preferred Domain knowledge relevant to the energy sector and working knowledge of Oil and Gas value chain eg upstream midstream or downstream and associated business workflows Proven ability to frame data science opportunities leverage standard foundational tools and Azure services to perform exploratory data analysis for purposes of data cleaning and discovery visualize data and identify actions to reach needed results Ability to quickly assess current state and apply technical concepts across cross functional business workflows Experience with driving successful execution deliverables and accountabilities to meet quality and schedule goals Ability to translate complex data into actionable insights that drive business val Demonstrated ability to engage and establish collaborative relationships both inside and outside immediate workgroup at various organizational levels across functional and geographic boundaries to achieve desired outcomes Demonstrated ability to adjust behavior based on feedback and provide feedback to other Team oriented mindset with effective communication skills and the ability to work collaboratively Strong problem solving skills and attention to detail Excellent communication and collaboration skills ML Engineer Key Responsibilities Transform data science prototypes into appropriate scale solutions in a production environment Orchestrate and configure infrastructure that assists Data Scientists and analysts in building low latency scalable and resilient machine learning and optimization workloads into an enterprise software product Combine expertise in mathematics statistics computer science and domain knowledge to create advanced AIML models Collaborate closely with the AI Technical Manager and GCC Petro technical professionals and data engineers to integrate and scale models into the business framework Identify data appropriate technology and architectural design patterns to solve business challenges using approved standard analytical tools and AI design patterns and architectures Partner with Data Scientists and IT Foundational services to implement complex algorithms and models into enterprise scale machine learning pipelines Run machine learning experiments and finetune algorithms to ensure optimal performance Consistently deliver complex innovative and complete solutions driving them through design planning development and deployment that simplify business processes and workflows to drive business value Work collaboratively with a large variety of different teams including data scientists data engineers and solution architects from various organizations within business units and IT Required Qualification Minimum 2 years experience in Object Oriented Design andor Functional Programming in Python 2 to 5 years of experience Mature software engineering skills such as source control versioning requirement spec architecture and design review testing methodologies CICD etc Must have a disciplined methodical minimalist approach to designing and constructing layered software components that can be embedded within larger frameworks or applications Experience implementing machine learning frameworks and libraries such as MLflow Experience with containers and container managements docker Kubernetes Experience developing cloud first solutions using Microsoft Azure Services including building machine learning pipelines in Azure Machine Learning and or Fabric Hands on experience in deploying machine learning pipelines with Azure Machine Learning SDK Working knowledge of mathematics primarily linear algebra probability statistics and algorithms Proficient at orchestrating largescale MLDL jobs leveraging big data tooling and modern container orchestration infrastructure to tackle distributed training and massive parallel model executions on cloud infrastructure Experience designing custom APIs for machine learning models for training and inference processes and designing implementing and delivering frameworks for MLOps Experience with model lifecycle management and automation to support retraining and model monitoring Experience implementing and incorporating ML models on unstructured data using cognitive services and or computer vision as part of AI solutions and workflows History of working with large scale model optimization and hyperparameter tuning applied to MLDL models Knowledge of enterprise SaaS complexities including security access control scalability high availability concurrency online diagnoses deployment upgrade migration internationalization and production support Knowledge of data engineering and transformation tools and patterns such as Databricks Spark Azure Data Factory Ability to engage other technical experts at all organizational levels and assess opportunities to apply machine learning and analytics to improve business workflows and deliver information and insight to support business decisions Ability to communicate in a clear concise and understandable manner both orally and in writing