AI ML Engineer

We are looking for a versatile AI/ML Engineer to join our growing AI Service Line. In this role, you will bridge the gap between innovation and execution. You will not only work on client delivery projects, building robust predictive and generative solutions, but also play a key role in our Center of Excellence (CoE) by building accelerators, best practices, and thought leadership assets.

The ideal candidate is a builder who has moved beyond notebook prototyping and knows how to deploy scalable, production-grade AI applications.

Key Responsibilities

1. Delivery & Solution Engineering (Traditional & GenAI)

Generative AI: Design and build LLM-powered applications using RAG (Retrieval-Augmented Generation), Agents, and fine-tuning techniques. Experience with frameworks like LangChain, LlamaIndex, and flow engineering.

Traditional ML: Develop and tune classical machine learning models (Regression, Classification, Clustering, Time-Series) for business use cases such as churn prediction, demand forecasting, and fraud detection.

End-to-End Execution: Own the lifecycle of AI development from data ingestion and feature engineering to model training and evaluation.

2. Productionalization & MLOps

PoC to Prod: Take successful Proof of Concepts (PoCs) and re-engineer them for production environments. This includes code refactoring, optimization for latency/cost, and error handling.

Deployment: Deploy models as scalable APIs (FastAPI/Flask) and manage containerization (Docker/Kubernetes).

Ops: Implement CI/CD pipelines for ML and set up monitoring for model drift, data quality, and hallucination checks.

3. Center of Excellence (CoE) Contribution

Asset Creation: Build reusable code libraries, templates, and frameworks that speed up delivery for the wider team.

Innovation: Stay updated with the latest research (e.g., SLMs, Agentic workflows, Multi-modal models) and conduct internal PoCs to validate their business viability.

Mentorship: Guide junior engineers and conduct code reviews to ensure high-quality standards.

Required Qualifications

Experience:

3-5 years of hands-on experience in Data Science and Machine Learning engineering.

Minimum 1+ years of dedicated experience working with Large Language Models (LLMs) and Generative AI stacks.

Technical Skills:

Core AI/ML: Strong grasp of statistics and algorithms (Random Forest, XGBoost, K-Means, etc.) using Scikit-Learn, Pandas, and NumPy.

GenAI Stack: Deep familiarity with OpenAI API, Anthropic, or open-source models (Llama 3, Mistral) via HuggingFace. Proficiency in Vector Databases (Pinecone, Milvus, ChromaDB) and Embedding strategies.

Coding: Advanced proficiency in Python and SQL.

MLOps & Cloud: Experience with AWS (SageMaker/Bedrock), Azure (OpenAI Service/ML Studio), or GCP (Vertex AI). Familiarity with MLflow, Docker, and Git actions.

Soft Skills:

Ability to translate complex technical concepts into business value for stakeholders.

Problem-solving mindset with a focus on delivering practical, working solutions rather than just theoretical models.

Preferred Qualifications (Bonus)

Experience fine-tuning SLMs/LLMs (LoRA, QLoRA).

Knowledge of Graph Databases (Neo4j) for GraphRAG implementations.

Certifications in Cloud AI/ML (AWS/Azure/GCP).