AI/ML Engineer (Er_Ai)

Job Title: AI/ML Engineer (LLMs, Automation & Agentic Systems)

Experience: 3- 6+ Years

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