AI ML Engineer

AI ML Engineer at Ergobite

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.

Responsibilities

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 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 with LangChain, LangGraph, or agent orchestration frameworks.
• Hands-on experience with Flask / FastAPI APIs.
• Strong knowledge of PostgreSQL and vector databases.
• Experience with 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 optimisation.
• Exposure to real-time/event-driven architectures.
• Background in fintech/compliance/transaction monitoring systems.

Key skills/competency

• AI ML Engineering
• Intelligent Automation
• LLM Applications
• RAG Pipelines
• Agentic Workflows
• Model Fine-tuning
• Python
• PyTorch TensorFlow
• LangChain LangGraph
• API Development