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Salary
$37k – $84k per year (Estimated)
Location
In office (Hyderabad)
Seniority
Senior
Employment
Full-Time
Overview
Company
Impact
Profile match
Weekday is an Indian recruitment company that sources software engineers through referrals from other engineers rather than through job advertisements or agency databases. Its model pays working engineers to vouch for former colleagues they rate, turning informal knowledge about who is genuinely good into a searchable candidate pool that companies can hire from. Based in Bengaluru and backed by Y Combinator, the platform has layered AI screening and outbound sourcing on top of that referral network, and sells to startups and technology companies hiring in the Indian market.

This role is for one of Weekday’s clients

Salary range: Rs 1800000 - Rs 2200000 (ie INR 18 - 22 LPA)

Min Experience: 8+ years

Location: Hyderabad

JobType: full-time

We are looking for a highly experienced Senior GenAI Developer with 8-12 years of overall software development experience and strong expertise in Generative AI, LangChain, and LangGraph. The ideal candidate will be responsible for designing, developing, and deploying intelligent AI-powered applications, agentic workflows, and scalable solutions that leverage modern Large Language Models (LLMs).

You will work closely with product, engineering, data, and business teams to translate complex requirements into production-ready GenAI solutions. This role requires strong hands-on development skills, an understanding of AI/ML concepts, and the ability to build reliable, scalable, and maintainable AI systems.

Requirements

Key Responsibilities

  • Design, develop, and deploy production-grade Generative AI applications using LLMs, prompt engineering, retrieval-augmented generation (RAG), and AI agents.
  • Build sophisticated AI workflows and autonomous agent systems using LangChain and LangGraph.
  • Develop multi-step, stateful, and tool-enabled agentic workflows capable of reasoning, decision-making, and task execution.
  • Integrate LLMs with enterprise applications, APIs, databases, vector stores, and external tools.
  • Design and implement RAG pipelines including document ingestion, chunking, embedding generation, vector search, retrieval, and contextual response generation.
  • Evaluate and optimize prompts, model performance, response quality, latency, reliability, and cost.
  • Work with different LLM providers and models and select appropriate approaches based on business and technical requirements.
  • Develop reusable frameworks, components, and APIs for GenAI capabilities across products and use cases.
  • Implement safeguards, validation mechanisms, monitoring, and evaluation frameworks to improve the reliability of AI-generated outputs.
  • Collaborate with cross-functional teams to understand business requirements and convert them into scalable AI solutions.
  • Mentor junior developers and contribute to technical design, architecture discussions, and engineering best practices.
  • Stay current with emerging developments in Generative AI, agentic AI, LLM architectures, and AI engineering.

Must-Have Skills

  • 8-12 years of experience in software development, AI/ML engineering, or related technical roles.
  • Strong hands-on expertise in Generative AI and Large Language Models (LLMs).
  • Extensive experience working with LangChain for building LLM-powered applications and workflows.
  • Strong expertise in LangGraph for developing stateful, multi-agent, and agentic AI applications.
  • Strong understanding of prompt engineering, RAG, embeddings, vector databases, and semantic search.
  • Proficiency in Python and experience developing scalable backend services and APIs.
  • Experience integrating LLMs with APIs, databases, enterprise systems, and external tools.
  • Strong understanding of LLM inference, context management, function/tool calling, and model evaluation.
  • Experience designing production-ready AI systems with appropriate testing, monitoring, security, and performance optimization.

Good-to-Have Skills

  • Experience with cloud platforms such as AWS, Azure, or GCP.
  • Familiarity with vector databases such as Pinecone, Weaviate, Milvus, Chroma, or FAISS.
  • Experience with Docker, Kubernetes, CI/CD, and cloud-native application development.
  • Knowledge of agent evaluation, observability, guardrails, and responsible AI practices.
  • Exposure to multimodal AI, fine-tuning, model adaptation, or open-source LLMs.
  • Experience working with FastAPI or similar Python-based backend frameworks.

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related technical discipline.

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