226 Mlops Jobs - Page 3

Curious about the role? What your typical day would look like? 6+ years of relevant DS experience Proficient in a structured Python Proficient in any one of cloud technologies is mandatory (AWS/ Azure/GCP) Follows good software engineering practices and has an interest in building reliable and robust software Good understanding of DS concepts and DS model lifecycle Working knowledge of Linux or Unix environments ideally in a cloud environment Working knowledge of Spark/ PySpark is desirable Model deployment / model monitoring experience is mandatory CI/CD pipeline creation is good to have Excellent written and verbal communication skills

We are seeking a hands-on, forward-thinking Machine Learning Technical Lead with deep expertise in traditional ML, NLP, Generative AI, and causal inference, and a strong understanding of multimodal large language models (LLMs) capable of processing both image and text data. The ideal candidate will lead the development, scaling, and evaluation of cutting-edge AI systems that deliver high business value from complex, high-volume datasets. This role involves team leadership, cloud-native ML deployment (AWS/Azure), statistical rigor, and clear communication of AI-driven insights to both technical and non-technical stake holders Key Responsibilities: Lead the design, development, and deployment of machine learning systems covering: Predictive modelling NLP tasks: NER, summarization, classification Generative AI: LLMs with prompt engineering and fine-tuning Causal inference and impact evaluation Multimodal modelling using vision-language models (e.g., CLIP, BLIP, GPT-4V) for use cases that involve image and text inputs Build and scale models that support impact evaluation, uplift modelling, and causal inference, using frameworks like Do Why, EconML, or CausalNex. Collaborate with stakeholders to define, quantify, and evaluate the causal impact of product interventions, policy changes, or ML-driven decisions. Architect scalable ML pipelines on AWS or Azure, handling high-throughput, multi-source data ingestion and model orchestration. Guide the integration of LLMs and prompt engineering into business workflows (e.g., classification, summarization, generative reasoning). Conduct deep exploratory data analysis (EDA) using statistical techniques: hypothesis testing, correlation analysis, regression diagnostics. Ensure robust MLOps practices including CI/CD, automated retraining, performance monitoring, and rollback strategies. Present AI findings, impact estimates, and strategic implications to technical peers, executives, and cross-functional partners. Mentor and manage a team of data scientists and ML engineers, fostering best practices and continuous innovation. Required Skills and Qualifications: Bachelors or Master’s in Computer Science, Statistics, Data Science, or related quantitative field. 6+ years of experience in AI/ML development, with 2+ years in a technical leadership capacity. Hands-on experience with: Predictive modelling (e.g., regression, XGBoost, tree-based models) NLP systems (e.g., NER, summarization, classification) Generative AI models (e.g., GPT, LLaMA) and prompt engineering Causal inference frameworks (e.g., DoWhy, EconML, Pyro) Strong statistical foundation with expertise in experimental design, counterfactual analysis, and impact evaluation. Proficiency in Python, PySpark, and libraries like scikit-learn, Hugging Face Transformers, and REST framework Candidate must be in Chennai or in the nearby districts, or in the nearby states of Kerala, Karnataka and willing to relocate to Chennai

Experience - 8 - 10 years Location - Bengaluru Key Responsibilities Design, implement, and maintain end-to-end MLOps pipelines for model training, validation, deployment, and monitoring. Build and manage LLMOps pipelines for fine-tuning, evaluating, and deploying large language models (e.g., OpenAI, HuggingFace Transformers, custom LLMs). Use Kubeflow and Kubernetes to orchestrate reproducible, scalable ML/LLM workflows. Implement CI/CD pipelines for ML projects using GitHub Actions , Argo Workflows , or Jenkins . Automate infrastructure provisioning using Terraform , Helm , or similar IaC tools. Integrate model registry and artifact management with tools like MLflow , Weights & Biases , or DVC . Manage containerization with Docker and container orchestration via Kubernetes . Set up monitoring , logging , and alerting for production models using tools like Prometheus , Grafana , and ELK Stack . Collaborate closely with Data Scientists and DevOps engineers to ensure seamless integration of models into production systems. Ensure model governance, reproducibility, auditability, and compliance with enterprise and legal standards. Conduct performance profiling, load testing, and cost optimization for LLM inference endpoints. Required Skills and Experience Core MLOps/LLMOps Expertise 5+ years of hands-on experience in MLOps/DevOps for AI/ML. 2+ years working with LLMs in production (e.g., fine-tuning, inference optimization, safety evaluations). Strong experience with Kubeflow Pipelines , KServe , and MLflow . Deep knowledge of CI/CD pipelines with GitHub Actions , GitLab CI , or CircleCI . Expert in Kubernetes , Helm , and Terraform for container orchestration and infrastructure as code. Programming & Frameworks Proficient in Python , with experience in ML libraries such as scikit-learn , TensorFlow , PyTorch , Hugging Face Transformers . Familiarity with FastAPI , Flask , or gRPC for building ML model APIs. Cloud & DevOps Hands-on with AWS , Azure , or GCP (preferred: EKS, S3, SageMaker, Vertex AI, Azure ML). Knowledge of model serving using Triton Inference Server , TorchServe , or ONNX Runtime . Monitoring & Logging Tools: Prometheus , Grafana , ELK , OpenTelemetry , Sentry . Model drift detection and A/B testing in production environments. Soft Skills Strong problem-solving and debugging skills. Ability to mentor junior engineers and collaborate with cross-functional teams. Clear communication, documentation, and Agile/Scrum proficiency. Preferred Qualifications Experience with LLMOps platforms like Weights & Biases , TruEra , PromptLayer , LangSmith . Experience with multi-tenant LLM serving or agentic systems (LangChain, Semantic Kernel). Prior exposure to Responsible AI practices (bias detection, explainability, fairness).

The Distinguished Engineer for Emerging Technologies is a senior-level engineering leadership role responsible for shaping and guiding the strategic direction, technical architecture, and adoption of emerging technologies across the conversational chatbot and search business unit, with a primary focus on Artificial Intelligence (AI), Generative AI (GenAI), and Agentic AI. This role involves working closely with senior executives, cross-functional engineering teams, and key business units to build robust, scalable, and innovative technology solutions that drive the companys strategic initiatives. The Distinguished Engineer will be a thought leader in the conversational AI space, leveraging deep technical expertise to drive breakthroughs in applied AI technologies, mentor teams and drive innovations. What youll be doing You will be overseeing design, development, and implementation of complex software systems and enterprise-level applications and drive digital transformation. This role offers an exceptional opportunity to influence, innovate, and lead in an environment dedicated to pushing the boundaries of AI technology in conversational and enterprise search space. Performing cross-functional, multidisciplinary technical oversight to include evaluation of project work, scope, resources, methods and associated procedures related to org level initiatives. Understanding technical and functional architecture of current systems in Verizon Consumer business and bring in architecture recommendation for building intelligent conversational agents for Chatbot applications across different platforms (web, mobile, voice assistant) catering to customer sales and support applications Define and lead the technology strategy for Conversational Chatbot and Search, with a focus on advancing AI, Gen AI and Agentic AI initiatives Drive strategy for Conversational Chatbot and Search application. Provide strategic insights on AI trends, advancements, and competitive positioning to the executive leadership team. Identifying new technologies, continuous evaluation of evolving industry trends, and best practices, which can be applied in the applications and drive overall IT/Business benefits. Drive innovation and thought leadership in the team and building a strong pipeline for innovation/ideas and help guide the teams navigate from conceptual to implementation phases. Drive hackathons and ideathons at Org level. Influence and align cross-functional teams and business units to ensure cohesive execution of AI-driven objectives and goals. Partnering with Associate fellows and architects across consumer business on several org level initiatives. Mentoring and developing next level Principal Engineers, fostering a culture of technical excellence and continuous learning in emerging technologies will also be a core aspect of this role. Publish white papers, patents and research findings to position the organization as an industry leader in emerging technologies Working with startups to drive partnerships in developing Niche solutions for Consumer Business. Where youll be working In this hybrid role, you'll have a defined work location that includes work from home and assigned office days set by your manager. You'll need to have: Masters degree or Six or more years of work experience. Six or more years of experience in the roles of Technical/Solutions Architect principles and practices of application design and systems integration. Six or more years of experience of leading design, conceptualizing, designing and architecture of complex IT systems solutions. Six or more years of relevant experience engaging in product, technology and Industry evaluations. Experience in 4G & 5G Wireless Telecom especially in RAN network along with RF Engineering skills Experience in handling multiple stakeholders and large vendor teams Experience in handling contact center solutions specifically chatbot, call center applications and tools, Web Application Solution and AI/GenAI Solution deployments at scale. Five or more years of experience in defining architecture and building Conversational AI solutions with latest technologies like Google Dialog Flow, ReactJS, Springboot based microservices Strong background in systems architecture, data engineering, and cloud-native technologies (AWS, GCP, Azure), application performance monitoring using industry leading tools and data visualization. Expertise in AI, machine learning, deep learning, natural language processing, generative AI techniques, Autonomous experiences (Agentic AI) and Digital Twins Proficiency in Python, TensorFlow, PyTorch, and other AI/ML frameworks. Experience with MLOps, data ethics, and large-scale AI deployment. Experience in filing patents and holding white papers and patents. Affiliations with architecture and design industry forums. Awareness of Security and Legal requirements related to IT solutions Experience in driving innovation in organizations via hackathons, ideathons and technology evangelism. Experience providing guidance to the delivery teams in articulating Solution Architecture and converting them into implementable Technical Designs. Even better if you have one or more of the following: Certifications in the specific technology areas. Ability to represent the team/application areas in various technology forums both internal and external. Strong verbal and written communication skills with skills in negotiations and building strong consensus with all stakeholders.Experience in playing the role of a technology person and a vivid speaker who has represented in technology forums. Experience in consulting roles to drive digital adoption across multiple business domains. Strong relationship skills and collaborative style to enable success across multiple partners. Strong commercial mindset and orientations to driving business value (revenue growth and expense mitigation).

Role & responsibilities Agentic AI Development : Work on building scalable multi-modal Large Language Model (LLM) based AI agents, leveraging frameworks such as LangGraph, Microsoft Autogen, or Crewai. AI Research and Innovation : Research and build innovative solutions to relevant AI problems, including Retrieval-Augmented Generation (RAG), semantic search, knowledge representation, tool usage, fine-tuning, and reasoning in LLMs. Technical Expertise : Proficiency in a technology stack that includes Python, LlamaIndex / LangChain, PyTorch, HuggingFace, FastAPI, Postgres, SQLAlchemy, Alembic, OpenAI, Docker, Azure, Typescript, and React. LLM and NLP Experience : Hands-on experience working with LLMs, RAG architectures, Natural Language Processing (NLP), or applying Machine Learning to solve real-world problems. Dataset Development : Strong track record of building datasets for training and/or evaluating machine learning models. Customer Focus : Enjoy diving deep into the domain, understanding the problem, and focusing on delivering value to the customer. Adaptability : Thrive in a fast-paced environment and are excited about joining an early-stage venture. Model Deployment and Management : Automate model deployment, monitoring, and retraining processes. Collaboration and Optimization : Collaborate with data scientists to review, refactor, and optimize machine learning code. Version Control and Governance : Implement version control and governance for models and data. Preferred candidate profile Bachelor's degree in computer science, Software Engineering, or a related field 4-8 years of experience in MLOps, DevOps, or related roles Have strong programming experience and familiarity with Python based deep learning frameworks like Pytorch, JAX, Tensorflow Have strong familiarity and knowledge of machine learning concepts Proficiency in cloud platforms (AWS, Azure, or GCP) and infrastructure-as-code tools like Terraform Experience with experiment tracking and model versioning tools You have experience with technology stack: Python, LlamaIndex / LangChain, PyTorch, HuggingFace, FastAPI, Postgres, SQLAlchemy, Alembic, OpenAI, Docker, Azure, Typescript, React. Knowledge of data pipeline orchestration tools like Apache Airflow or Prefect Familiarity with software testing and test automation practices Understanding of ethical considerations in machine learning deployments Strong problem-solving skills and ability to work in a fast-paced environment

ML Engineer (ML Ops)Chennai / Bangalore / Hyderabad Curious about the role? What your typical day would look like? We are looking for a Machine Learning Engineer/Sr MLE 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 Engage with clients to understand their business context. Translate business problems and technical constraints into technical requirements for the desired analytics solution. Collaborate with a team of data scientists and engineers to embed AI and analytics into the business decision processes. What do we expect? 6+ years of experience with at least 4+ years of relevant MLOps experience . Proficient in a structured Python (Mandate) Proficient in any one of cloud technologies is mandatory ( AWS/ Azure/ GCP) Proficient in Azure Databricks Follows good software engineering practices and has an interest in building reliable and robust software. Good understanding of DS concepts and DS model lifecycle. Working knowledge of Linux or Unix environments ideally in a cloud environment. Working knowledge of Spark/ PySpark is desirable. Model deployment / model monitoring experience is desirable. CI/CD pipeline creation is good to have. Excellent written and verbal communication skills. B.Tech from Tier-1 college / M.S or M. Tech is preferred. You are important to us, lets stay connected! Every individual comes with a different set of skills and qualities so even if you don’t tick all the boxes for the role today, we urge you to apply as there might be a suitable/unique role for you tomorrow.We are an equal-opportunity employer. Our diverse and inclusive culture and values guide us to listen, trust, respect, and encourage people to grow the way they desire. Note: The designation will be commensurate with expertise and experience. Compensation packages are among the best in the industry.Additional Benefits: Health insurance (self & family), virtual wellness platform, and knowledge communities.

Overview In this role, we are seeking a Senior 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 strong expertise in offshore execution, cost optimization, automation strategies, and cross-functional collaboration to drive operational excellence. Manage and execute DataOps programs, ensuring alignment with business objectives, data governance standards, and enterprise data strategy. Oversee real-time monitoring, automated alerting, and self-healing mechanisms to improve system reliability and performance. Develop and enforce governance models and operational frameworks to streamline service delivery and execution roadmaps. Drive 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. Support proactive issue identification and self-healing automation, enhancing the sustainment capabilities of the PepsiCo Data Estate. Responsibilities Manage and oversee offshore teams delivering DataOps, BIOps, Data IntegrationOps, FinOps, AIOps, MLOps, and SRE initiatives to drive operational excellence. Implement governance frameworks, define KPIs, and establish operational SLAs to ensure efficiency and quality in offshore execution. Drive process standardization, cost optimization, and automation adoption to enhance service scalability and effectiveness. Collaborate with onshore teams, business leaders, and stakeholders to ensure seamless execution and alignment of offshore deliverables with business goals. Optimize resource utilization by leveraging automation and AI-driven insights to improve productivity and streamline operations. Ensure continuous improvement, risk mitigation, and compliance adherence across offshore programs to maintain operational integrity. Act as a key liaison between IT, business leaders, data stewards, and compliance teams to ensure alignment with regulatory and security requirements. Monitor and enhance end-to-end Data Operations and sustainment processes, including testing, monitoring, and support for global data products. Manage day-to-day DataOps activities, ensuring adherence to SLAs, incident resolution, and engaging with SMEs to meet business demands. Contribute to work intake and Agile management processes, supporting data platform teams in executing strategic initiatives effectively. Foster strong relationships with senior stakeholders and executives, ensuring transparency, proactive risk assessment, and continuous communication. Collaborate across teams to address cloud infrastructure and data service challenges, ensuring high system availability and performance. Develop and automate operational policies and crisis management functions to minimize downtime and enhance incident response. Champion a customer-obsessed culture, advocating for high-quality service delivery and continuous process enhancements. Build and develop a high-performing team, fostering a diverse and agile work environment that aligns with business objectives. Adapt quickly to changing priorities, ensuring teams remain productive and focused on key deliverables. Leverage cloud and high-performance computing expertise to establish trust, drive innovation, and enhance the overall customer experience. Qualifications 12+ years of technology experience in a large-scale global organization, preferably in the CPG industry. 8+ years of experience in Data & Analytics, with a strong understanding of data engineering, data management, and operations. 7+ years of cross-functional IT experience, collaborating across multiple teams and stakeholders. 5+ years of leadership/management experience, overseeing teams and driving operational excellence. Familiarity with Site Reliability Engineering (SRE) principles, including automated issue resolution and scalability improvements. Excellent communication skills, with the ability to empathize with stakeholders and explain technical issues to varied audiences. Strong customer focus, advocating for end-user needs and delivering high-quality experiences. Proactive problem-solving mindset, taking ownership of issues and driving resolution. Ability to learn and adapt in a fast-paced environment, staying up to date with emerging technologies and methodologies. Experience in technical support and operations for mission-critical solutions in a Microsoft Azure environment. Familiarity with Site Reliability Engineering (SRE) principles, including automated issue resolution and scalability improvements. Proven ability to drive operational excellence, ensuring stability and performance in complex enterprise environments. Experience managing large-scale operational services in dynamic and evolving technology landscapes. Strategic thinking capabilities, focusing on cost efficiency, operational effectiveness, and delivery speed. Ability to develop and execute strategic plans, aligning technology roadmaps with business objectives. Strong relationship-building skills, fostering trust and collaboration across IT and business functions. Proven ability to align business and IT priorities, identifying mutually beneficial solutions. Experience leading cross-functional and virtual teams, effectively communicating vision and objectives. Demonstrated success in delivering high-impact results in complex and transformational projects. Experience with multi-country/global implementations, particularly involving data and analytics. Understanding of master data management, data governance, and analytics frameworks. Knowledge of data acquisition, data cataloging, and data management tools. Strong influencing and negotiation skills, with the ability to engage and persuade stakeholders at all levels.

Job Title: AI/Gen AI Engineer (Python) Location: Bangalore, India(Onsite) Job Description: We are seeking a highly skilled AI/Gen AI Engineer with 7+ years of experience in Python and extensive expertise in AI/Generative AI frameworks. The ideal candidate should have strong proficiency in data manipulation libraries such as Pandas and NumPy, along with hands-on experience in implementing and managing Large Language Models (LLMs). Key Responsibilities: • Develop and maintain AI/Gen AI solutions using Python. • Work with LLMs, ensuring their effective implementation and optimization. • Utilize FastAPI for API development and integration. • Apply software engineering best practices, including version control (Git), CI/CD pipelines, and automated testing. • Implement MLOps and ML engineering strategies to streamline AI model deployment and monitoring. • Leverage DevOps tools such as Docker, Kubernetes, Jenkins, and Terraform for efficient deployment and scaling. • Ensure robust CI/CD practices for AI/ML workflows. Required Skills & Experience: • 7+ years of experience in Python development. • Strong understanding of AI/Gen AI frameworks. • Hands-on experience with LLMs. • Expertise in data manipulation using Pandas and NumPy. • Proficiency in FastAPI for API development. • Strong grasp of software engineering principles, including Git, CI/CD, and automated testing. • Experience in MLOps, ML engineering, and AI model lifecycle management. • Proficiency in DevOps tools such as Docker, Kubernetes, Jenkins, and Terraform., and Swagger is must • Strong understanding of JavaScript programming language • Experience with SQL and NoSQL databases • Experience with RESTful API design and development • Experience with unit testing and test-driven development (Jest) • Excellent problem-solving and debugging skills. • Ability to work independently and as part of a team. • Strong communication and interpersonal skills • Experience with cloud computing platforms such as AWS or Azure • Experience with container orchestration technologies such as Docker and Kubernetes • Experience with continuous integration and continuous delivery (CI/CD) pipelines • Experience with leading and mentoring development teams • Strong knowledge of integration patterns, technologies, and tools • Experience with integration platforms such as MuleSoft, Dell Boomi, or similar. • Experience with API management platforms such as Apisix, Apigee, Kong, or similar. • Experience with cloud-based integration solutions such as AWS, Azure, or similar. • Strong understanding of microservices architecture and RESTful APIs • Experience in designing and implementing complex integration solutions for enterprise-level applications

3+ years of work experience in Python programming for AI/ML, deep learning, and Generative AI model development Proficiency in TensorFlow/PyTorch, Hugging Face Transformers and Langchain libraries Hands-on experience with NLP, LLM prompt design and fine-tuning, embeddings, vector databases and agentic frameworks Strong understanding of ML algorithms, probability and optimization techniques 6+ years of experience in deploying models with Docker, Kubernetes, and cloud services (AWS Bedrock, SageMaker, GCP Vertex AI) through APIs, and using MLOps and CI/CD pipelines Familiarity with retrieval-augmented generation (RAG), cache-augmented generation (CAG), retrieval-integrated generation (RIG), low-rank adaptation (LoRA) fine-tuning Ability to write scalable, production-ready ML code and optimized model inference Experience with developing ML pipelines for text classification, summarization and chat agents Prior experience with SQL and noSQL databases, and Snowflake/Databricks

Key Responsibilities Data Pipeline Development: Design, develop, and optimize robust data pipelines to efficiently collect, process, and store large-scale datasets for AI/ML applications. ETL Processes: Develop and maintain Extract, Transform, and Load (ETL) processes to ensure accurate and timely data delivery for machine learning models. Data Integration: Integrate diverse data sources (structured, unstructured, and semi-structured data) into a unified and scalable data architecture. Data Warehousing & Management: Design and manage data warehouses to store processed and raw data in a highly structured, accessible format for analytics and AI/ML models. AI/ML Model Development: Collaborate with Data Scientists to build, fine-tune, and deploy machine learning models into production environments. Focus on model optimization, scalability, and operationalization. Automation: Implement automation techniques to support model retraining, monitoring, and reporting. Cloud & Distributed Systems: Work with cloud platforms (AWS, Azure, GCP) and distributed systems to store and process data efficiently, ensuring that AI/ML models are scalable and maintainable in the cloud environment. Data Quality & Governance: Implement data quality checks, monitoring, and governance frameworks to ensure the integrity and security of the data being used for AI/ML models. Collaboration: Work cross-functionally with Data Science, Business Intelligence, and other engineering teams to meet organizational data needs and ensure seamless integration with analytics platforms. Required Skills and Qualifications Bachelor's or Masters Degree in Computer Science, Engineering, Data Science, or a related field. Strong proficiency in Python for AI/ML and data engineering tasks. Experience with AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and Keras. Proficient in SQL and working with relational databases (e.g., MySQL, PostgreSQL, SQL Server). Strong experience with ETL pipelines and data wrangling in large datasets. Familiarity with cloud-based data engineering tools and services (e.g., AWS (S3, Lambda, Redshift), Azure, GCP). Solid understanding of big data technologies like Hadoop, Spark, and Kafka for data processing at scale. Experience in managing and processing both structured and unstructured data. Knowledge of version control systems (e.g., Git) and agile development methodologies. Experience with data containers and orchestration tools such as Docker and Kubernetes. Strong communication skills to collaborate effectively with cross-functional teams. Preferred Skills Experience with Data Warehouses (e.g., Amazon Redshift, Google BigQuery, Snowflake). Familiarity with CI/CD pipelines for ML model deployment and automation. Familiarity with machine learning model monitoring and performance optimization. Experience with data visualization tools like Tableau, Power BI, or Plotly. Knowledge of deep learning models and frameworks. DevOps or MLOps experience for automating deployment of models. Advanced statistics or math background for improving model performance and accuracy.

Position: Architect AI & ML Experience- 8+ Years Job Location- Pune We are seeking a dynamic and experienced leader to drive the growth of our AI practice with a focus on Generative AI, advanced NLP solutions, and Large Language Models (LLMs). This role is ideal for a seasoned professional who combines technical expertise with exceptional leadership skills, customer-facing experience, and a vision for scaling teams and capabilities. Skills & Qualifications Technical Expertise: Extensive experience with NLP techniques and multi-class/multi-label text classification. Hands-on experience fine-tuning private LLMs (e.g., LLaMA, Gemma). Proficiency in PyTorch, Hugging Face, LangChain, Haystack, and related frameworks. Strong knowledge of model orchestration tools like MLFlow or KubeFlow. Familiarity with RAG techniques and vector stores (e.g., Pinecone, ChromaDB). Strategic Mindset: Visionary thinking with the ability to translate emerging AI trends into actionable business strategies. Good to have: Experience with multi-modal AI models (e.g., image-text, text-audio models). Expertise in Databricks for scalable model development and deployment. Knowledge of Explainability AI tools (e.g., Captum) for interpretable models. Key Responsibilities Leadership & Growth: Lead, mentor, and grow a high-performing AI team, fostering innovation and collaboration. Develop and execute strategies to expand the practice and deliver measurable business value to clients. Customer Engagement: Serve as a confident and articulate interface with clients, ensuring clear communication of AI strategies, solutions, and outcomes. Build trusted partnerships with clients, understanding their needs and aligning solutions to their goals. AI Solution Development: Design and implement state-of-the-art NLP solutions, focusing on multi-class and multi-label text classification. Fine-tune and deploy private LLMs (e.g., LLaMA, Gemma) for tailored business applications. Develop Retrieval-Augmented Generation (RAG) pipelines leveraging vector databases like Pinecone or ChromaDB for high-performance solutions. Operational Excellence: Oversee end-to-end model lifecycle management, including training, deployment, and monitoring. Integrate explainability into AI models, ensuring transparency and trust in decision-making. Collaborate with external LLM providers (e.g., OpenAI, Claude) to enhance and integrate AI capabilities. Leadership & Communication: Proven ability to lead and inspire teams, with a track record of scaling AI practices. Exceptional communication and presentation skills, capable of engaging diverse stakeholders confidently. Additional Skills Experience with multi-modal AI models (e.g., image-text, text-audio models). Expertise in Databricks for scalable model development and deployment. Knowledge of Explainability AI tools (e.g., Captum) for interpretable models.

Were on the lookout for Computer Vision Engineers with strong Kotlin development experience and a passion for innovation in the retail domain. Join our dynamic team to build intelligent vision-based systems that transform customer experience, streamline operations, and deliver real-time insights at scale. We're Hiring: Computer Vision Engineer (Kotlin) Retail Domain Location: [Specify your current Location or Remote] Experience: 3–7 Years/ 3 to 10 Years Open Positions: 2–4 Employment Type: Full-time Budget & Client: Will be discussed during the interview process Joining: Immediate to short notice preferred Responsibilities -Design and implement advanced computer vision algorithms for object -detection, tracking, and recognition in real-world retail settings. -Develop and maintain Kotlin-based applications for mobile and edge devices. -Collaborate with product managers, data scientists, and engineering teams to deliver end-to-end solutions. -Optimize models for real-time inference on edge platforms (e.g., Jetson Nano, Raspberry Pi, Android). -Analyze large-scale video/image datasets to extract meaningful insights. -Contribute across the full software development lifecycle: architecture, development, testing, and deployment. Required Skills & Qualifications -Bachelor’s/Master’s in Computer Science, Electrical Engineering, or related field -3+ years of experience in computer vision and image processing -Strong programming skills in Kotlin (Android or cross-platform apps) -Hands-on with OpenCV, TensorFlow, or PyTorch -Experience in deploying models on mobile/edge devices -Must have experience in retail use cases (e.g., shelf monitoring, customer analytics, smart checkout) -Excellent communication, analytical, and teamwork skills Preferred (Good to Have): -Experience with cloud platforms (AWS, GCP, Azure) -Familiarity with MLOps & CI/CD pipelines -Exposure to AR/VR or 3D vision technologies -Contributions to open-source CV projects Interested? Drop your updated CV at anzia.sabreen@bct-consulting.com or DM us directly. Let’s build the future of retail with computer vision!

About the Role: Looking for a highly experienced Computer Vision Specialist / AI Engineer to join our dynamic team. The ideal candidate will have a proven track record of delivering real-world computer vision and deep learning solutions, particularly in smart monitoring and edge & cloud AI applications. This role demands deep technical expertise in designing and deploying vision-based solutions such as object detection, object tracking, facial recognition, behavioral analysis, ANPR and OCR. You will lead the development and deployment of scalable, production-grade AI systems. Lead the architecture, development, and deployment of computer vision systems from concept to production. Develop and optimize advanced vision algorithms and deep learning models for real-time performance. Optimize AI/ML models for deployment across edge, cloud, and hybrid environments using tools like TensorRT, TFLite, OpenVINO, and ONNX. Collaborate with hardware and software engineering teams to ensure system-wide performance optimization. Conduct thorough testing, benchmarking, and performance tuning of models and systems. Required Skills & Experience: 25 years of hands-on experience in Computer Vision, Deep Learning, and AI model deployment. Strong proficiency in Python , with production-level experience in writing clean, modular, and scalable code. Familiar with frameworks and tools such as OpenCV, TensorFlow, PyTorch, YOLO, MediaPipe, Dlib. In-depth knowledge of: Convolutional Neural Networks (CNNs) o Object detection and tracking o Facial recognition techniques o Video-based behavioral analysis RTSP streaming, CCTV camera systems, NVR/DVR integration Deployment experience on platforms including: Edge devices (NVIDIA Jetson, Raspberry Pi, Coral Edge TPU) o Cloud (AWS/Azure/GCP) with containerization tools (Docker) Strong understanding of MLOps tools and workflows, including model tracking, data versioning, monitoring, and retraining. Nice to Have: Familiar with LLM and Vision Language Models

Build and deploy scalable ML models and MLOps pipelines in collaboration with data scientists Required Candidate profile 6–12 yrs in ML development, Python, model tuning, and enterprise AI deployment.

Preferred candidate profile 1. LLM Basics : (Llama, Gemini ) : Understand the basics of generative AI and LLMs, such as key terminology, uses, potential issues, and primary frameworks. One should know what the data is trained on and any potential biases/issues that there may be with the data . Knowledge on know exactly how big LLMs can be, how computationally expensive training will be, and the differences between training LLMs and machine learning models. 1. Prompt Engineering : Knowledge on designing inputs for LLMs once theyre developed. 2. Prompt Engineering with OpenAI : As a leading figure in LLMs and generative AI, it’s important to know how to use prompt engineering specifically with OpenAI tools, as you’ll likely be using them at some point in your career. 3. Question-Answering :Question-answering (QA) LLMs are a type of large language model that has been trained specifically to answer questions. 4. Fine-Tuning : Knowledge on Fine-tuning to improve the performance of an LLM on a variety of tasks, including text generation, translation, summarization, and question-answering. Customize LLMs for specific applications, such as customer service chatbots or medical diagnosis systems. Awareness on supervised learning. This involves providing the LLM with a dataset of labelled data, where each data point is a pair of input and output.. 1. Lang Chain : To architect complex LLM pipelines by chaining multiple models together (Classification, text generation, code generation, etc.)`Agents` to interact with all these external systems to execute actions dictated by LLMs. 2. Parameter Efficiency/Tuning : LORA 3. RAG Building : Generative AI, mastering RAG building—short for Retrieval-Augmented Generation—is becoming increasingly crucial 4. ML OPS and in particular LLMOps : Large Language Model Operations, is the practice of managing and maintaining large language models (LLMs) in a production setting 5. TensorFlow i s like a versatile toolbox for creating intelligent programs that can learn and understand various concepts, including machine learning, deep learning, and data science

Locations We are located in Austin (USA), Singapore, and Hyderabad (India). Job Locations: Hyderabad, Ahmedabad, Indore Role Overview We are looking for a highly skilled Senior Data Scientist with a passion for deploying and maintaining machine learning models, leading teams, and driving innovation. Summary: Years of Experience: Minimum of 8+ years Job Location: Hyderabad, Ahmedabad, Indore Notice Period: Immediate to 30 days Work Shift: General Shift What Youll Do Key Responsibilities: Lead the strategic direction of machine learning infrastructure with a focus on performance, scalability, and reliability. Drive innovation by developing advanced machine learning solutions. Collaborate with the Data Science team to deploy, maintain, and optimize ML models in production environments. Architect and oversee the development of real-time and batch inference systems. Mentor mid-level engineers, fostering growth and technical excellence. Conduct comprehensive code reviews and encourage a culture of continuous learning. Communicate insights and translate complex technical concepts into actionable business solutions. Stay ahead of industry trends, integrating new technologies and tools. Technical Skills Extensive experience with machine learning methodologies and infrastructure. Expertise in Python, TensorFlow, PyTorch, and other ML frameworks. Strong knowledge of real-time data processing and pipeline development. Proficiency in cloud platforms (AWS, GCP, Azure) for ML model deployment. Hands-on experience with Kubernetes, Docker, and container orchestration. Deep understanding of MLOps/DevOps best practices, including CI/CD pipelines, testing, and version control. Soft Skills Strong analytical and problem-solving skills. Excellent communication and collaboration abilities. Proven leadership experience, mentoring engineering teams to success. Ability to effectively translate technical concepts into actionable business solutions. Passion for continuous learning and staying updated with industry advancements. What Youll Bring Education: Bachelors or Masters degree in Computer Science, Data Science, or a related field. Proven experience in large-scale machine learning projects. Deep knowledge of deploying scalable, robust systems for real-time and batch inference use cases. Location: Jaipur

Work mode – Currently this is remote (WFH) but it’s not permanent WFH , once business ask the candidate to come to office, they must relocate. Mandatory:- ML Engineer, MLOPS , end to end deployment, CI/CD, Docker and Kubernetes, model deployment Required Candidate profile Lead end-to-end development & delivery of Machine-Learning applications, emphasizing operations and monitoring. Deployment and operation of ML applications, following CI/CD best practices.

Job description MLOps Engineer Bangalore (work at office) 5+ Years experience relevant in MLOPs Mandatory Skills: Strong proficiency in Generative AI, Large Language Models (LLMs), deep learning, agentic frameworks, and RAG setup. Experience in designing and implementing machine learning models using scikit-learn and TensorFlow. Hands-on expertise with AI/ML frameworks such as Hugging Face, LangChain, LangGraph, and PyTorch. Cloud AI services experience, particularly in AWS SageMaker and AWS Bedrock. MLOps & DevOps: Knowledge of data pipeline setup, Apache Airflow, CI/CD, and containerization (Docker, Kubernetes). API Development: Ability to develop and maintain APIs following RESTful principles. Technical proficiency in Elastic, Python, YAML, and system integrations. Nice to Have: Experience with Observability, Ansible, Terraform, Git, Microservices, AIOps, and scripting in Python. Familiarity with AI cloud services such as Azure OpenAI and Google Vertex AI.

Hi Everyone Greetings from Intuition IT Global Recruitment Firm We hav an Exciting Job opportunity for Devops with AI Platform and Data science with our leading Client Location PAN India (Remote) Job Type: Long term Contract Job Description : Support Platform which offers infrastructure to Data science/Data Analytics/MLOps teams Resolution of issues for provisioning of new use cases in AI Platform Resolution of incidents and services requests related to AI Platform Collaborate with IAM teams for accounts provisioning Co-ordinate with other teams from Platform - AWS, Snowflake, Data bricks etc Monitor CI/CD pipelines in AI Platform Proficient in tools like AWS ( IAM, S3, EKS, SAGEMAKER, ACM, ECR, RDS, Secrets Manager, Lambda, Step Functions) DevOps tools - Jenkins, Bitbucket, Jfrog, SonarQube, Checkmarx, Kubernetes, Docker etc responsibilities Please share cv to this email id : maheshwari.p@intuition-IT.com Preferred candidate profile

Embark on an exciting journey into the realm of software development with 3Pillar! We extend an invitation for you to join our team and gear up for a thrilling adventure. As an AI/ML Lead, you will manage a team of data scientists, ML engineers, and developers to build AI-powered features, drive PoCs to production, and collaborate with stakeholders to deliver measurable business impact. If you are passionate about discovering solutions hidden in large data sets to improve business outcomes, consider this your pass to the captivating world of Data Science and Engineering! Role & responsibilities Lead a team of AI ML engineers, providing technical guidance, mentorship, and support. Collaborate directly with client teams to understand business problems, explain technical solutions clearly, and lead the implementation of AI/ML features while representing 3Pillars technical excellence. Lead technical execution of AI/ML projects from experimentation to production. Translate business problems into AI/ML solutions and manage solution delivery. Guide team members on model selection, evaluation, fine-tuning, and Gen AI usage. Collaborate with product, data engineering, and software teams for integrated delivery. Conduct code reviews, ensure best practices, and contribute to model deployment pipelines. Maintain documentation and model governance as per responsible AI standards. Define project goals, scope, and deliverables in collaboration with stakeholders. Create scalable architecture and robust machine learning systems that meet business objectives of a software product. Collaborate with cross-functional teams to integrate machine learning solutions into existing products and workflows. Establish best practices and standards for machine learning development within the organization. Monitor and evaluate the performance of machine learning models in production and implement improvements as needed. Stay updated on industry trends and advancements in machine learning technologies. Implement, deploy, optimize AI/ML algorithms and models into production environments. Conduct experiments, perform data analysis, and present findings to stakeholders. Qualifications: Bachelors degree in Computer Science, Engineering, Mathematics, or a related field; a Masters degree is preferred. 8+ years of experience in building AI models with ML, NLP and deep learning. Proven track record of successfully delivering machine learning projects from concept to production. Strong leadership, interpersonal skills, and ability to effectively communicate technical concepts to non-technical stakeholders. Excellent problem-solving abilities and attention to detail. Strong knowledge of AI/ML techniques, algorithms, and frameworks. Experience building and deploying ML models (classification, NLP, forecasting). Experience with cloud platforms such as AWS, Azure, or Google Cloud. Strong programming skills in Python, and familiarity with libraries such as PyTorch, TensorFlow, and Scikit-Learn. Solid understanding of machine learning concepts, algorithms, and statistical methods. Experience with computer vision libraries (OpenCV, etc.) and NLP libraries (spaCy, Hugging Face, etc.). Knowledge of MLOps practices and experience with CI/CD tools. Good understanding of using LLMs via APIs or fine-tuning open-source models. Hands-on with data prep (Pandas, Spark), feature engineering, model training and tuning. Knowledge of vector stores, embeddings, and LLMOps tools (LangChain, LlamaIndex, etc.). Experience with at least one cloud platform and CI/CD for ML (GitHub Actions, MLflow, etc.). Exposure to monitoring frameworks ( evidently.ai , whylogs, or custom dashboards). Proven ability to work independently and with a team. Benefis: Imagine a flexible work environment whether it's the office, your home, or a blend of both. From interviews to onboarding, we embody a remote-first approach. You will be part of a global team, learning from top talent around the world and across cultures, speaking English everyday. Our global workforce enables our team to leverage global resources to accomplish our work in efficient and effective teams. Were big on your well-being – as a company, we spend a whole trimester in our annual cycle focused on wellbeing. Whether it is taking advantage of fitness offerings, mental health plans (country-dependent), or simply leveraging generous time off, we want all of our team members operating at their best. Our professional services model enables us to accelerate career growth and development opportunities - across projects, offerings, and industries. We are an equal opportunity employer. It goes without saying that we live by values like Intrinsic Dignity and Open Collaboration to create cutting-edge technology AND reinforce our commitment to diversity - globally and locally. Thank you, Kiran Dhanak 3Pillar

Role : Principal ML Ops Architect Responsibilities : 1. Strategic Leadership :a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities.b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes.c. Foster a culture of innovation and continuous improvement within the ML Ops team. 2. Technical Architecture :a. Design and implement scalable, reliable, and efficient ML Ops architectures.b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle.c. Ensure compliance with industry best practices and standards for ML Ops. 3. Team Management :a. Lead and mentor a team of ML Ops engineers and architects.b. Foster collaboration and knowledge sharing among team members.c. Provide technical guidance and support to data scientists and engineers. 4. Innovation and Research :a. Stay up-to-date with emerging ML Ops trends and technologies.b. Research and evaluate new tools and techniques to enhance ML Ops capabilities.c. Contribute to the development of innovative ML Ops solutions. Minimum Required Skills :- 11+ years of experience preferred. - Proven track record of designing and implementing large-scale ML pipelines and infrastructure.- Experience with distributed computing frameworks (Spark, Hadoop)- Knowledge of graph databases and auto ML libraries- Bachelor's / Master's degree in computer science, analytics, mathematics, statistics - Strong experience in Python, SQL.- Solid understanding and knowledge of containerization technologies (Docker, Kubernetes).- Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow)- Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch). - Certifications in cloud platforms or ML technologies can be a plus.- Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).- Strong problem-solving and analytical skills.- Ability to plan, execute and take ownership of task. ML Ops / MLOps Architect- Azure DevOps- Docker- Kubernetes- TensorFlow- MLFlow- Pipeline- Machine Learning Platform Engineer- Data Science Platform Engineer- DevOps Engineer (with ML focus)- AI Engineer- Data Engineer- Cloud Engineer (with ML focus)- Software Engineer (with ML focus)- Model Deployment Specialist- MLOps Architect- CI/CD- PyTorch- Scikit-learn- Cloud Computing- Big Data- Azure- Azure Machine Learning- GCP- Vertex AI- AWS- Amazon SageMaker

Role : Principal ML Ops Architect Responsibilities : 1. Strategic Leadership :a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities.b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes.c. Foster a culture of innovation and continuous improvement within the ML Ops team. 2. Technical Architecture :a. Design and implement scalable, reliable, and efficient ML Ops architectures.b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle.c. Ensure compliance with industry best practices and standards for ML Ops. 3. Team Management :a. Lead and mentor a team of ML Ops engineers and architects.b. Foster collaboration and knowledge sharing among team members.c. Provide technical guidance and support to data scientists and engineers. 4. Innovation and Research :a. Stay up-to-date with emerging ML Ops trends and technologies.b. Research and evaluate new tools and techniques to enhance ML Ops capabilities.c. Contribute to the development of innovative ML Ops solutions. Minimum Required Skills :- 11+ years of experience preferred. - Proven track record of designing and implementing large-scale ML pipelines and infrastructure.- Experience with distributed computing frameworks (Spark, Hadoop)- Knowledge of graph databases and auto ML libraries- Bachelor's / Master's degree in computer science, analytics, mathematics, statistics - Strong experience in Python, SQL.- Solid understanding and knowledge of containerization technologies (Docker, Kubernetes).- Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow)- Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch). - Certifications in cloud platforms or ML technologies can be a plus.- Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).- Strong problem-solving and analytical skills.- Ability to plan, execute and take ownership of task. ML Ops / MLOps Architect- Azure DevOps- Docker- Kubernetes- TensorFlow- MLFlow- Pipeline- Machine Learning Platform Engineer- Data Science Platform Engineer- DevOps Engineer (with ML focus)- AI Engineer- Data Engineer- Cloud Engineer (with ML focus)- Software Engineer (with ML focus)- Model Deployment Specialist- MLOps Architect- CI/CD- PyTorch- Scikit-learn- Cloud Computing- Big Data- Azure- Azure Machine Learning- GCP- Vertex AI- AWS- Amazon SageMaker

Your responsibilities As a Senior ML Engineer, You will be expected to perform the following tasks and responsibilities in a manner consistent with CBA's Values and People Capabilities. Core Responsibilities: • AI/ML Strategy Implementation: Support CDO in executing the Group's AI strategy Drive E2E AI use cases with cutting-edge technologies Align solutions with Group's data strategy and architecture ML Platform Development: Design and implement scalable ML platforms Build automated ML pipelines using Python and AWS services Ensure robust model deployment and monitoring systems Cloud Infrastructure Management: Utilize AWS services (CloudFormation/Terraform, S3, CloudWatch, IAM, EC2, ECR, Lambda, EMR, SageMaker) Implement infrastructure as code practices Maintain security and compliance standards API Development & Integration: Develop RESTful APIs using Django, Langchain , and Node.js Create efficient data processing pipelines Implement MLOps best practices Technical Leadership: Provide technical guidance on ML infrastructure Collaborate with data scientists and business stakeholders Drive innovation in AI/ML technology adoption Your skills & Your Experience Required Skills & Experience: 7-10 years of experience in ML Engineering/MLOps Expert-level Python programming skills Strong experience with AWS services and cloud architecture Proficiency in shell scripting and automation Experience with API development frameworks Knowledge of ML frameworks and tools Understanding of DevOps practices Technical Competencies: Advanced proficiency in IT Risk Assessment & Management Advanced Systems Management capabilities Sound consulting and critical analysis skills Strong negotiation and service partner management abilities People Capabilities: Customer Focus Team and Culture building Continuous Improvement mindset Effective Communication Strong Judgement Results-Driven approach Good to Have: Basic understanding of database concepts, SQL Domain experience in finance, banking, Insurance Good understanding of AI/ML concepts and GenAI foundations Your Qualifications Essential: • A degree in Engineering / Computer Science or a related discipline • Minimum 3 years of Experience in IT Industry, with hands on experience in ML Models development & Its maintenance & Data Science. Desirable: • Specialties: Big Data, CI/CD, AWS /Cloud Services. • Experience in Development (Coding, Testing) • Additional industry or product certification Your Development If you live the values and demonstrate the people capabilities, we can offer great opportunities. Whether you want to move across the organisation or up into a leadership role, the ways you live the values and demonstrate the people capabilities are key to progressing. From this role, you could transition into other roles to broaden your technology coverage as part of developing yourself for more senior role either in same portfolio or different portfolio. Use the people and enterprise services capabilities required for this role as a guide to the critical skills and behaviours you need for your next move. Proficient in FastAPI, MLOps, and Python. API Development & Integration: Develop RESTful APIs using Django, Langchain and Node.js; Create efficient data processing pipelines; Implement MLOps best practices Strong background in machine learning and data science. Experience with building and implementing data models and algorithms. Testing and validating models. Familiarity with AWS SageMaker. Preferences: Candidates with a background in statistics and mathematics may not be a good fit. Not primarily looking for GCP expertise. AI Ops experience is not a priority but acceptable. Certification: Any associate-level cloud certification is acceptable. Open for candidates with 60-90days notice period. Note: Require 100% Diversity profiles .

Role : Principal ML Ops Architect Responsibilities : 1. Strategic Leadership : a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes. c. Foster a culture of innovation and continuous improvement within the ML Ops team. 2. Technical Architecture : a. Design and implement scalable, reliable, and efficient ML Ops architectures. b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle. c. Ensure compliance with industry best practices and standards for ML Ops. 3. Team Management : a. Lead and mentor a team of ML Ops engineers and architects. b. Foster collaboration and knowledge sharing among team members. c. Provide technical guidance and support to data scientists and engineers. 4. Innovation and Research : a. Stay up-to-date with emerging ML Ops trends and technologies. b. Research and evaluate new tools and techniques to enhance ML Ops capabilities. c. Contribute to the development of innovative ML Ops solutions. Minimum Required Skills : - 11+ years of experience preferred. - Proven track record of designing and implementing large-scale ML pipelines and infrastructure. - Experience with distributed computing frameworks (Spark, Hadoop) - Knowledge of graph databases and auto ML libraries - Bachelor's / Master's degree in computer science, analytics, mathematics, statistics - Strong experience in Python, SQL. - Solid understanding and knowledge of containerization technologies (Docker, Kubernetes). - Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow) - Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch). - Certifications in cloud platforms or ML technologies can be a plus. - Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes). - Strong problem-solving and analytical skills. - Ability to plan, execute and take ownership of task. Keywords : - ML Ops / MLOps Architect - Azure DevOps - Docker - Kubernetes - TensorFlow - MLFlow - Pipeline - Machine Learning Platform Engineer - Data Science Platform Engineer - DevOps Engineer (with ML focus) - AI Engineer - Data Engineer - Cloud Engineer (with ML focus) - Software Engineer (with ML focus) - Model Deployment Specialist - MLOps Architect - CI/CD - PyTorch - Scikit-learn - Cloud Computing - Big Data - Azure - Azure Machine Learning - GCP - Vertex AI - AWS - Amazon SageMaker

Role : Principal ML Ops Architect Responsibilities : 1. Strategic Leadership : a. Define and drive the overall ML Ops strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of ML Ops platforms, frameworks, and processes. c. Foster a culture of innovation and continuous improvement within the ML Ops team. 2. Technical Architecture : a. Design and implement scalable, reliable, and efficient ML Ops architectures. b. Select and integrate appropriate tools, technologies, and frameworks to support the ML lifecycle. c. Ensure compliance with industry best practices and standards for ML Ops. 3. Team Management : a. Lead and mentor a team of ML Ops engineers and architects. b. Foster collaboration and knowledge sharing among team members. c. Provide technical guidance and support to data scientists and engineers. 4. Innovation and Research : a. Stay up-to-date with emerging ML Ops trends and technologies. b. Research and evaluate new tools and techniques to enhance ML Ops capabilities. c. Contribute to the development of innovative ML Ops solutions. Minimum Required Skills : - 11+ years of experience preferred. - Proven track record of designing and implementing large-scale ML pipelines and infrastructure. - Experience with distributed computing frameworks (Spark, Hadoop) - Knowledge of graph databases and auto ML libraries - Bachelor's / Master's degree in computer science, analytics, mathematics, statistics - Strong experience in Python, SQL. - Solid understanding and knowledge of containerization technologies (Docker, Kubernetes). - Proficient in Experience in CI/CD pipelines, model monitoring, and MLOps platforms (Kubeflow, MLFlow) - Proficiency in cloud platforms, containerization, and ML frameworks (TensorFlow, PyTorch). - Certifications in cloud platforms or ML technologies can be a plus. - Extensive experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes). - Strong problem-solving and analytical skills. - Ability to plan, execute and take ownership of task. Keywords : - ML Ops / MLOps Architect - Azure DevOps - Docker - Kubernetes - TensorFlow - MLFlow - Pipeline - Machine Learning Platform Engineer - Data Science Platform Engineer - DevOps Engineer (with ML focus) - AI Engineer - Data Engineer - Cloud Engineer (with ML focus) - Software Engineer (with ML focus) - Model Deployment Specialist - MLOps Architect - CI/CD - PyTorch - Scikit-learn - Cloud Computing - Big Data - Azure - Azure Machine Learning - GCP - Vertex AI - AWS - Amazon SageMaker