About the role
Key Responsibilities:
• Build, evaluate, and deploy machine learning and deep learning models for production. • Develop LLM-based and agentic applications, including RAG pipelines and tool use.
• Fine-tune and optimise models for accuracy, latency, and cost using PEFT and quantisation.
• Design evaluation frameworks, benchmarks, and guardrails to safeguard model quality.
• Build and automate data pipelines for training, inference, and model monitoring.
• Package and serve models as scalable APIs with containerisation, CI/CD, and MLOps.
• Stay updated with advancements in AI/ML and apply them to practical use cases.
RequirementsEducational Qualifications:
• B.E./B.Tech, M.Tech, B.Sc., M.Sc., MCA, or an equivalent degree in Computer Science, Information Technology,
Engineering, Mathematics, Statistics, or a related quantitative
field.
• Relevant certifications in cloud, AI/ML, or software
engineering are a plus.
Skillset:
• Strong programming skills in Python.
• Experience with ML/DL frameworks (e.g., scikit-learn, PyTorch,
or TensorFlow).
• Hands-on experience with LLM APIs, prompt engineering, and
agent frameworks (e.g., LangChain, LangGraph, MCP).
• Working knowledge of RAG, embeddings, and vector
databases (e.g., pgvector, FAISS, or Pinecone).
• Experience serving models as APIs (FastAPI, Docker) with
MLOps practices (MLflow, model versioning, monitoring).
• Proficiency with SQL and data pipeline tooling for large
datasets.
• Experience with at least one cloud platform (AWS, Azure, or
GCP).
Soft Skills:
• Adaptability in a fast-evolving technology landscape.
• Strong problem-solving ability with a structured, hands-on
approach.
• Critical thinking to evaluate trade-offs and validate AI
generated outputs.
Benefits
- Opportunity to work with a dynamic and fast-paced engineering IT organization.
- Be part of a company that is passionate about transforming product development with technology.