AI/ML Engineer
- Compensation
- Not disclosed
- Location
- Bengaluru
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 1+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- Python with Flask , plus pandas and scikit-learn
- LLM APIs (Claude, OpenAI, Gemini): tool calling, structured outputs, streaming, and prompt caching
- Agent frameworks (LangGraph, Claude Agent SDK, or OpenAI Agents SDK), and MCP
- Production RAG: chunking, hybrid search, reranking, and vector databases (pgvector or Qdrant)
- Evals: test sets, LLM-as-judge, and regression checks before release
- LLM observability (Langfuse or LangSmith)
- Classic ML: classification, forecasting, and anomaly detection
- Docker and Linux
- Daily use of AI coding tools, with the ability to explain and review generated code
Nice to have
- Self-hosting models with vLLM or Ollama, including quantization
- Fine-tuning with LoRA or QLoRA
- Guardrails: prompt injection defense and PII redaction
- Multimodal models (vision, speech)
- Kubernetes, GitLab CI, Prometheus/Grafana
Full description
We are looking for an AI/ML Engineer with 2-3 years of experience, including at least 1 year on LLM or ML projects, to join our team in Bangalore. You will design, build, and ship AI features and agents into production, and youll work on both LLM-based systems and classic machine learning models. This role suits someone who cares about how AI performs in the real world, not just in a demo, and who measures quality, cost, and latency as seriously as functionality.
Must have:
- Python with Flask , plus pandas and scikit-learn
- LLM APIs (Claude, OpenAI, Gemini): tool calling, structured outputs, streaming, and prompt caching
- Agent frameworks (LangGraph, Claude Agent SDK, or OpenAI Agents SDK), and MCP
- Production RAG: chunking, hybrid search, reranking, and vector databases (pgvector or Qdrant)
- Evals: test sets, LLM-as-judge, and regression checks before release
- LLM observability (Langfuse or LangSmith)
- Classic ML: classification, forecasting, and anomaly detection
- Docker and Linux
- Daily use of AI coding tools, with the ability to explain and review generated code
Good to have:
- Self-hosting models with vLLM or Ollama, including quantization
- Fine-tuning with LoRA or QLoRA
- Guardrails: prompt injection defense and PII redaction
- Multimodal models (vision, speech)
- Kubernetes, GitLab CI, Prometheus/Grafana
What we look for: Ownership, improving on the spec rather than just meeting it, raising risks early, and learning without being asked.
- Ship AI features and agents end to end, from design to production
- Write evals before shipping, then track quality, cost, and latency in production
- Pick the right model for each task based on cost, latency, and accuracy
- Protect against prompt injection and data leaks, and meet privacy and governance rules
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.