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.
The Candidate Will Have Responsibilities Across The Following Functions
Problem Identification and Solution Design:
- Understand business problems and design AI-driven automation solutions.
- Architect scalable systems combining ML models, LLMs, and rule-based logic.
Data Collection And Preprocessing
- Collect, clean, and preprocess structured and unstructured data.
- Build pipelines for document ingestion, embeddings, and retrieval systems.
Model Development And Training
- Develop and fine-tune ML, NLP, and Generative AI models.
- Work LLMs and SLMs (Small Language Models) for optimised use cases.
- Apply fine-tuning techniques (LoRA, PEFT) for efficient model adaptation.
- Implement embedding models, semantic search, and ranking systems.
RAG And Knowledge Systems
- Design and implement RAG (Retrieval-Augmented Generation) pipelines.
- Work on vector databases and hybrid retrieval strategies.
- Build or knowledge graphs for enhanced reasoning.
Agentic AI And Orchestration
- Build agent-based systems using LangChain, LangGraph, or similar frameworks.
- Design multi-agent workflows, tool usage, and orchestration pipelines.
- Implement agent capabilities, memory, planning, and reasoning loops.
Model Evaluation And Validation
- Evaluate models precision, recall, F1-score, and LLM-specific eval methods.
- Reduce hallucinations and improve response quality using prompt and system design.
Deployment And Integration
- Build and deploy APIs with Flask / FastAPI.
- Integrate PostgreSQL and vector databases (FAISS, Pinecone, Chroma, etc. )
- Deploy cloud platforms (AWS/GCP/Azure) or on-prem/local environments.
Monitoring And Optimisation
- Monitor performance (accuracy, latency, cost) and continuously improve systems.
- Optimise pipelines, prompts, and models for production readiness.
Ethical AI And Compliance
- Ensure fairness, bias mitigation, and safe AI practices.
- Implement guardrails and compliance-aware AI systems.
Requirements
- Strong proficiency in Python.
- Hands-on experience with ML frameworks (PyTorch / TensorFlow).
- Experience LLMs, SLMs, embeddings, and RAG pipelines.
- Strong understanding of fine-tuning techniques (LoRA, PEFT).
- Experience LangChain, LangGraph, or agent orchestration frameworks.
- Hands-on experience with Flask / FastAPI APIs.
- Strong knowledge of PostgreSQL and vector databases.
- Experience automation systems/decision engines / rule-based systems.
Good To Have
- Experience MLOps practices and tools (CI/CD for ML, model versioning, monitoring).
- Familiarity with knowledge graphs (Neo4j, etc. )
- Experience local/on-prem LLM deployment and optimisation.
- Exposure to real-time/event-driven architectures.
- Background in fintech/compliance/transaction monitoring systems.
This job was posted by Shivani Bhoras from Ergobite.
