Overview

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.

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Qualifications

• 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