AI / ML Engineer
Ergobite · Pune Division, Maharashtra, India
- On site
- Full-time
- $130,000 / year
- Pune Division, Maharashtra, India
Job highlights
- Build AI-driven automation and LLM applications.
- Design and deploy RAG pipelines and agentic workflows.
- Develop and fine-tune ML, NLP, and Generative AI models.
- Integrate PostgreSQL, vector databases, and APIs.
- Ensure ethical AI practices and system optimization.
About the role
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
Skills & topics
- AI Engineer
- ML Engineer
- Python
- PyTorch
- TensorFlow
- LLM
- RAG
- LangChain
- Generative AI
- Automation
How to get hired
- 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.
Technical preparation
Behavioral questions
Frequently asked questions
- What specific AI/ML frameworks should I highlight for the Ergobite AI ML Engineer role?
- For the AI ML Engineer position at Ergobite, you should emphasize your hands-on experience with Python and ML frameworks such as PyTorch and TensorFlow. Highlighting your work with LLMs, SLMs, embeddings, and RAG pipelines is also crucial. Familiarity with fine-tuning techniques like LoRA and PEFT is a strong plus.
- How important is experience with agent orchestration frameworks for this AI ML Engineer job at Ergobite?
- Experience with agent orchestration frameworks like LangChain and LangGraph is highly valued for this AI ML Engineer role at Ergobite. The job description specifically mentions building agent-based systems and designing multi-agent workflows, indicating this is a key area of focus for the team.
- What kind of RAG pipeline experience is Ergobite looking for in an AI ML Engineer?
- Ergobite is seeking an AI ML Engineer with experience in designing and implementing scalable RAG (Retrieval-Augmented Generation) pipelines. This includes working with vector databases, implementing hybrid retrieval strategies, and potentially building knowledge graphs for enhanced reasoning capabilities.
- Does Ergobite prefer candidates with cloud deployment experience for their AI ML Engineer role?
- Yes, experience deploying AI/ML systems on cloud platforms like AWS, GCP, or Azure is beneficial for the AI ML Engineer role at Ergobite. The role also includes deploying to on-premise or local environments, so a versatile deployment background is advantageous.
- What are the key differences between ML models and LLMs that an AI ML Engineer should understand for this role?
- An AI ML Engineer at Ergobite needs to understand that traditional ML models often focus on specific tasks with structured data, while LLMs excel at understanding and generating human-like text across a wide range of tasks. This role requires integrating both, using LLMs for natural language understanding and generation, and ML models for other predictive or analytical tasks, often within hybrid AI systems.
- How can I best demonstrate my understanding of Ethical AI and compliance for the Ergobite AI ML Engineer position?
- To demonstrate your understanding of Ethical AI and compliance for the AI ML Engineer role at Ergobite, highlight any experience you have with fairness, bias mitigation, and implementing AI guardrails. Mention any projects where you've focused on ensuring responsible AI development and adherence to compliance standards.