SDE II - Gen AI

We are looking for an SDE II Gen AI Engineer who can build production-grade AI systems with a strong focus on agentic AI, multi-agent workflows, LLM applications, evaluations, clustering, open-weight model exploration, Python, FastAPI, and PyTorch. The candidate should be hands-on, strong in engineering, and comfortable building AI agents that can reason, use tools, call services, maintain context, and deliver reliable outputs in production.

Responsibilities:

• Build AI agents that can understand user intent, plan tasks, call tools/APIs, use memory, and generate structured responses.
• Design and develop multi-agent workflows where specialized agents handle tasks such as retrieval, reasoning, personalization, safety, evaluation, summarization, and orchestration.
• Build reusable agent skills/tools that can be plugged into an agent orchestration layer.
• Work on LLM-based applications for user query understanding, content generation, summarization, classification, recommendation, personalization, and decision support.
• Explore and benchmark open-weight model architectures, including decoder-only LLMs, encoder models, vision-language models, diffusion models, embedding models, and multimodal architectures.
• Build backend AI services using Python and FastAPI for agent execution, model inference, prompt generation, tool calling, evaluations, and orchestration.
• Work with PyTorch for model loading, inference, experimentation, fine-tuning, embeddings, and optimization.
• Build clustering and semantic grouping pipelines for content deduplication, topic discovery, user-interest grouping, article/event grouping, personalization, and retrieval improvement.
• Use embeddings, vector search, similarity scoring, and clustering techniques to improve retrieval, recommendations, personalization, and agent context selection.
• Build automated AI evaluation pipelines for LLM, GenAI, and agent outputs.
• Define and track evaluation metrics such as relevance, factuality, fluency, coherence, safety, consistency, tool-call accuracy, reasoning quality, and intent match.
• Implement LLM-as-judge, embedding-based evaluations, golden datasets, regression checks, and CI/CD quality gates.
• Design prompt templates, prompt versioning, structured outputs, fallback strategies, model routing, and guardrail logic.
• Optimize agent workflows for latency, cost, quality, safety, reliability, and scalability.
• Integrate AI agents with backend services, APIs, queues, databases, vector stores, storage systems, and observability tools.
• Work on agent memory, session state, context management, tool registry, planning, retry logic, and error handling.
• Collaborate with backend, ML, product, and platform teams to ship scalable AI-powered product experiences.

Requirements:

• Strong programming experience in Python.
• Hands-on experience with FastAPI.
• Working knowledge of PyTorch.
• Good understanding of LLMs, prompts, embeddings, vector search, and evaluations.
• Understanding of agentic AI, tool calling, RAG, memory, planning, and multi-agent workflows.
• Familiarity with open-weight model architectures and experimentation workflows.
• Basic understanding of clustering, similarity search, semantic grouping, and recommendation systems.
• Ability to build clean APIs and production-grade AI services.
• Good debugging, problem-solving, and system-thinking skills.

Good to Have:

• Experience with agent frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or custom agent orchestration systems.
• Experience with model serving, quantization, LoRA/fine-tuning, batching, caching, or inference optimization.
• Exposure to multimodal AI, image generation, video generation, vision-language models, or diffusion-based systems.
• Familiarity with Kubernetes, Docker, GCP/AWS, Pub/Sub/Kafka, CI/CD, and observability.
• Experience with evaluation tools or custom eval frameworks for LLMs and agent systems.
• Understanding of AI safety, content moderation, guardrails, and responsible AI practices.