Senior Ai Engineer & Data Scientist

We are looking for a highly skilled and versatile Senior AI Engineer & Data Scientist who can operate across the full spectrum of AI/ML — from classical machine learning and deep learning to cutting-edge Generative AI and Agentic AI systems. This is a senior role that combines hands-on technical leadership with people management; you will lead a team of 3–4 AI Engineers / Data Scientists while actively contributing to the design and delivery of production-grade intelligent systems.

The ideal candidate combines strong research intuition with solid engineering discipline and proven team leadership. You will design build and deploy ML/AI models architect robust data pipelines establish AI operations best practices spanning MLOps LLMOps and AgenticOps and mentor your team — all within modern cloud environments on AWS and Azure.

Key Responsibilities

Team Leadership & Collaboration

• Lead mentor and manage a team of 3–4 AI Engineers and Data Scientists fostering a culture of technical excellence and continuous learning.

• Conduct code reviews define engineering standards and drive sprint planning and delivery for the AI team.

• Collaborate with cross-functional stakeholders (Product Engineering Business) to translate business problems into AI/ML solutions.

• Report directly to the Head of AI providing regular updates on project progress team performance and technical roadmap.

Classical Machine Learning & Deep Learning

• Design develop and optimize supervised and unsupervised ML models (regression classification clustering recommendation systems time-series forecasting).

• Build and fine-tune deep learning models using frameworks such as PyTorch and TensorFlow for NLP Computer Vision and structured data tasks.

• Conduct rigorous experimentation feature engineering hyperparameter tuning and model evaluation to drive measurable business outcomes.

Generative AI & Agentic AI

• Architect and implement Generative AI solutions leveraging Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) and prompt engineering techniques.

• Design and build Agentic AI workflows — autonomous multi-step AI agents capable of reasoning tool use and dynamic decision-making.

• Evaluate fine-tune and deploy foundation models (e.g. GPT Claude LLaMA Mistral) for domain-specific applications.

• Implement guardrails evaluation frameworks and responsible AI practices for generative and agentic systems.

Data Engineering & Pipelines

• Design and build scalable reliable data pipelines for ingestion transformation and feature computation using tools like Apache Spark Airflow dbt or equivalent.

• Work with structured and unstructured data sources (databases APIs data lakes streaming platforms).

• Collaborate with Data Engineering teams to ensure data quality lineage and governance.

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AI Operations (MLOps / LLMOps / AgenticOps)

• Establish and maintain end-to-end AI operations pipelines covering traditional ML models LLM-based systems and agentic workflows.

• Implement experiment tracking model registry and automated retraining workflows using tools such as MLflow Kubeflow Weights & Biases or SageMaker Pipelines.

• Build monitoring and evaluation frameworks for LLMs and AI Agents — including latency tracking cost monitoring output quality scoring hallucination detection and agent behaviour observability.

• Define and track operational metrics for agentic systems: task success rate tool-call accuracy chain-of-thought reliability and end-to-end execution performance.

• Ensure production AI systems are reliable observable and performant with proper alerting drift detection and feedback loops.

Cloud & Infrastructure

• Deploy and manage AI/ML workloads on AWS and/or Azure cloud platforms.

• Leverage cloud-native AI/ML services (AWS SageMaker Bedrock Lambda Glue; Azure ML Azure OpenAI Service) for scalable model training and inference.

• Work with containerization (Docker Kubernetes) and Infrastructure-as-Code (Terraform CloudFormation) for reproducible environments.

Personnel Specification*

Education Bachelor’s or Master’s degree in Computer Science Data Science Machine Learning Statistics Mathematics or a related quantitative field.

Experience Experience 4–8 years of overall hands-on experience in Data Science and/or ML Engineering roles. Minimum 4 years of experience in Classical ML Deep Learning and Generative AI including LLMs RAG and Prompt Engineering. Minimum 1 year of experience in Agentic AI including designing building or deploying autonomous AI agent systems.

Experience leading or managing a small technical team of 3 members. Strong proficiency in Python and ML/DL libraries such as: • scikit-learn •PyTorch •TensorFlow •HuggingFace •LangChain • LlamaIndex Experience building and deploying production-grade ML models at scale. Hands-on experience with AWS and/or Azure AI/ML services such as: •SageMaker •Bedrock •AzureML • Azure OpenAI Service Understanding of data pipelines ETL/ELT data warehousing and AI Operations practices including model versioning LLM evaluation monitoring and CI/CD for ML. Experience with SQL/NoSQL databases and strong problem-solving communication and stakeholder management skills.

Skill Sets .

Strong proficiency in Python SQL and Bash; familiarity with Scala/Java is anadded advantage. .

Experience with ML/DL frameworks such as PyTorch TensorFlow scikit-learn XGBoost and Hugging Face Transformers. .

Hands-on experience with GenAI and Agentic AI frameworks such as LangChain LlamaIndex CrewAI and AutoGen along with LLM APIs. .

Experience with Data Engineering tools such as MS Fabric ADF and AWS Glue. .

Knowledge of AI Operations tools including MLflow Kubeflow DVC SageMaker Pipelines LangSmith and LangFuse. .

Hands-on experience with AWS and/or Azure cloud platforms and AI/ML services. .

Experience with Docker Kubernetes Terraform and CI/CD tools such as GitHub Actions and GitLab CI. .

Experience with relational NoSQL vector databases and data platforms such as PostgreSQL MongoDB Redis Pinecone ChromaDB Snowflake and Databricks.

Other Requirements (if any) .

Hands-on experience prototyping and deploying Agentic AI systems in production environments including multi-agent orchestration tool-use agents and autonomous task execution. .

Experience with vector databases such as Pinecone Weaviate Milvus and ChromaDB along with semantic search systems. .

Experience with multi-modal AI systems involving text image and audio. .

Contributions to open-source AI/ML projects are an added advantage. .

Experience with LLM/Agent evaluation and observability platforms such as LangSmith and LangFuse. .

Experience establishing AI Operations practices such as LLMOps and AgenticOps from the ground up.

Behavioural Competencies .

Strong analytical and problem-solving skills. .

Ability to work collaboratively in cross-functional teams. .

Strong communication and stakeholder management capabilities. .

Ability to lead and manage technical teams effectively.

Certifications .

Mandatory certifications must be acquired as per the Industrial Certification Policy of the company.

Other Remarks .

NA