Job Overview
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Job Description
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
How to Get Hired at Ergobite
- Tailor your resume: Highlight Python, ML frameworks, LLM, RAG, and agent experience.
- Showcase projects: Detail your work on intelligent automation and LLM applications.
- Prepare for technical questions: Review ML concepts, fine-tuning, and deployment strategies.
- Demonstrate problem-solving: Discuss how you've designed AI solutions for business problems.
- Ask insightful questions: Inquire about team collaboration and technical challenges.
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