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 robust 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 Agentic Ops, 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 Tensor Flow 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 / Agentic Ops)

• 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 Sage Maker 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 Sage Maker, Bedrock, Lambda, Glue; Azure ML, Azure OpenAI Service) for scalable model training and inference.

• Work with containerization (Docker, Kubernetes) and Infrastructure-as-Code (Terraform, Cloud Formation) 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 •Tensor Flow •Hugging Face •Lang Chain • Llama Index Experience building and deploying production-grade ML models at scale. Hands-on experience with AWS and/or Azure AI/ML services such as: •Sage Maker •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, Tensor Flow, scikit-learn, XGBoost, and Hugging Face Transformers. · Hands-on experience with GenAI and Agentic AI frameworks such as Lang Chain, Llama Index, CrewAI, and Auto Gen, 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, Sage Maker Pipelines, Lang Smith, and Lang Fuse. · 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 Git Hub Actions and Git Lab 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 Lang Smith and Lang Fuse. · Experience establishing AI Operations practices such as LLMOps and Agentic Ops 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

📌 Senior Ai Engineer & Data Scientist (India)
🏢 HIRESTAR JOB BANK
📍 India