Role Overview :

We are looking for a hands-on AI/ML Engineer with strong experience in building intelligent automation systems and modern LLM-powered applications. This role involves designing and deploying scalable RAG pipelines, agentic workflows, and hybrid AI systems (ML + LLM + rules) with model Fine-tuning experience for real-world production use cases.

Key Responsibilities :

1. Problem Identification & Solution Design :

- Understand business problems and design AI-driven automation solutions

- Architect scalable systems combining ML models, LLMs, and rule-based logic

2. Data Collection & Preprocessing :

- Collect, clean, and preprocess structured and unstructured data

- Build pipelines for document ingestion, embeddings, and retrieval systems

3. Model Development & Training :

- Develop and fine-tune ML, NLP, and Generative AI models

- Work with LLMs and SLMs (Small Language Models) for optimized use cases

- Apply fine-tuning techniques (LoRA, PEFT) for efficient model adaptation

- Implement embedding models, semantic search, and ranking systems

4. RAG & Knowledge Systems :

- Design and implement RAG (Retrieval-Augmented Generation) pipelines

- Work with vector databases and hybrid retrieval strategies

- Build or integrate knowledge graphs for enhanced reasoning

5. Agentic AI & Orchestration :

- Build agent-based systems using LangChain, LangGraph, or similar frameworks

- Design multi-agent workflows, tool usage, and orchestration pipelines

- Implement agent capabilities like memory, planning, and reasoning loops

6. Model Evaluation & Validation :

- Evaluate models using precision, recall, F1-score, and LLM-specific eval methods

- Reduce hallucinations and improve response quality using prompt and system design

7. Deployment & Integration :

- Build and deploy APIs using Flask / FastAPI

- Integrate with PostgreSQL and vector databases (FAISS, Pinecone, Chroma, etc.)

- Deploy on cloud platforms (AWS/GCP/Azure) or on-prem/local environments

8. Monitoring & Optimization :

- Monitor performance (accuracy, latency, cost) and continuously improve systems

- Optimize pipelines, prompts, and models for production readiness

9. Ethical AI & Compliance :

- Ensure fairness, bias mitigation, and safe AI practices

- Implement guardrails and compliance-aware AI systems

Required Skills :

- Strong proficiency in Python

- Hands-on experience with ML frameworks (PyTorch / TensorFlow)

- Experience with LLMs, SLMs, embeddings, and RAG pipelines

- Strong understanding of fine-tuning techniques (LoRA, PEFT)

- Experience with LangChain, LangGraph, or agent orchestration frameworks

- Hands-on experience with Flask / FastAPI APIs

- Strong knowledge of PostgreSQL and vector databases

- Experience building automation systems / decision engines / rule-based systems

Good to Have :

- Experience with MLOps practices and tools (CI/CD for ML, model versioning, monitoring)

- Familiarity with knowledge graphs (Neo4j, etc.)

- Experience with local/on-prem LLM deployment and optimization

- Exposure to real-time/event-driven architectures

- Background in fintech / compliance / transaction monitoring systems