JD:

Reporting Structure

Project Manager

Education

- Bachelor's Degree in Engineering

Experience

- 3 years in applied ML/AI/Gen AI lead roles with demonstrable production deployments;
- Mandatory experience with on-prem LLMs and model hosting, including fine-tuning and inference optimization.
- Proven expertise with Vector Databases and embedding pipelines for retrieval augmented generation and semantic search.
- Strong statistical foundations and experience designing experiments and evaluation frameworks.
- Proficiency in Python, PyTorch or Tensor Flow, and model serving frameworks.
- Familiarity with containerization and orchestration for on-prem environments.
- Proven experience and knowledge on LLM fine-tuning, embeddings, RAG, transformers.
- Hands-on experience with advanced RAG architectures, including hybrid search, semantic search, reranking, metadata filtering and retrieval optimization.
- Experience on Vector DBs: Elastic, Milvus, etc. and on indexing and similarity tuning.
- Experience evaluating retrieval performance using Precision@K, Recall@K, Mean Reciprocal Rank (MRR), NDCG and Hit Rate@K.
- Experience with Agentic AI, tool/function calling, multi-step workflows and orchestration frameworks such as Lang Chain and Lang Graph.
- MLOps: model registries, CI/CD, automated testing, monitoring and drift detection.
- Experience with model explainability tools, quantization, and inference acceleration.
- Familiarity with Open Shift or enterprise Kubernetes for on-prem deployments.

Industry

Preferably from a Technology, ITES, Products,



Skilled SI organization with a focus on BFSI.

Responsibilities

- • At least one production implementation of an AI, ML or Gen AI application with actual implementation experience and knowledge of various production grade application being used by End users
- • Define and track model metrics: accuracy, precision/recall, AUC, calibration, latency, throughput, business KPIs, and translate them into SLOs/SLA targets.
- • Design and operate on-prem LLMs: fine-tuning, prompt engineering, RAG architectures, embedding generation, and secure inference.
- • Integrate and manage Vector Databases: design embedding pipelines, select indexing strategies, tune similarity metrics, implement secure access controls, and monitor vector DB performance and cost.
- • Design and implement end-to-end RAG evaluation pipelines to measure retrieval relevance, answer correctness, faithfulness and hallucination rates.
- • Implement hybrid retrieval strategies combining keyword search, vector search and reranking to improve information retrieval accuracy.
- • Implement robust validation and governance: bias and fairness checks, explainability, model risk assessments, drift detection, and automated retraining triggers.
- • Productionize models with CI/CD, model registries and automated validation gates.
- • Collaborate with stakeholders across product, platform, security, and compliance to align models with business needs and regulatory constraints.

Certifications

Data Science, Data Analytics, Big Data Certifications are preferred

📌 Data Scientist (India)
🏢 GK HR Consulting India Private
📍 India