24 days ago
Pune, IndiaMid Level / Senior
Responsibilities
- Provide technical leadership and mentorship while collaborating with architects, product managers, UX designers, data scientists, and engineers.
- Own the end-to-end design, development, deployment, and release of AI-driven features.
- Design scalable, secure, and robust AI architectures and lead technical design discussions.
- Fine-tune, deploy, evaluate, monitor, and optimize LLMs for specific use cases.
- Build AI agents with RAG, multi-agent orchestration, tool use, planning, memory architectures, and external knowledge sources.
- Design and optimize vector databases, indexing algorithms, prompts, evaluation systems, observability, tracing, and guardrails.
- Integrate AI capabilities into customer-facing products and internal tools while improving scalability, performance, and cost efficiency.
- Track advances in LLMs, RAG, and generative AI and communicate technical concepts to technical and non-technical stakeholders.
Requirements
- 2–5 years of total experience with strong knowledge of LLMs, transformer architecture, attention mechanisms, and hyperparameter tuning.
- Bachelor’s degree in engineering or an equivalent degree from a recognized institute or university.
- Proven experience building AI agents, including multi-agent orchestration, tool-use patterns, multi-step planning, and short-term and long-term memory.
- Hands-on experience with LangGraph, CrewAI, AutoGen, or comparable agentic frameworks and RAG pipelines.
- In-depth knowledge of vector databases and indexing algorithms, with practical experience using FAISS, Pinecone, Weaviate, or Milvus.
- Experience with agent observability, tracing, and guardrails, including tools such as Langfuse or equivalent.
- Proficiency in prompt engineering, Python, TensorFlow, PyTorch, and Hugging Face Transformers.
- Experience with data preprocessing, vectorization, and large-scale datasets.
- Preferred experience with graph-based architectures, knowledge graph embeddings, graph neural networks, custom model training, cloud deployment, Docker, Kubernetes, programmatic advertising, RTB, ad auctions, MCP, or AI research contributions.
Benefits
- Hybrid work schedule with 3 days in the office and 2 days working remotely.
- Healthcare insurance, paternity and maternity leave, and broadband reimbursement.
- Office kitchen with healthy snacks and drinks and catered lunches.
Tech Stack
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