- Salary
- ₹23000k – ₹25000k
- Location
- Ahmedabad, India
- Department
- IT
- Seniority
- Senior
- Source
- Keka
Description
Role Overview:
Senior Software Engineer (AI) will design, build, and ship production-ready AI and LLM-powered applications. The role combines strong backend engineering with hands-on generative AI expertise—developing RAG pipelines, intelligent APIs, agentic workflows, and scalable cloud deployments while collaborating across product, data, design, and engineering teams
Key focus areas
1. AI and LLM engineering
- LLM integration: Build applications on large language models such as Azure OpenAI, OpenAI, Anthropic or open-source models. This includes prompt design, function and tool calling, and structured outputs.
- RAG (retrieval-augmented generation): Design retrieval pipelines that cover document ingestion, chunking, embeddings, vector search, re-ranking and grounding responses in trusted sources.
- AI agents and orchestration: Build agentic workflows using Claude/Co-pilot/Gemini.
- Evaluation and guardrails: Set up evaluation frameworks that measure accuracy, hallucination rate, latency and cost. Add safety filters, PII handling and content moderation.
2. Python and backend development
- Python: Strong skill in Python/Java/Php for AI development, including async programming, type hints and packaging.
- API development: Build RESTful APIs (FastAPI, Flask or ASP.NET Core) that expose AI features, with streaming responses, serialization and versioning.
- Application architecture: Understanding of dependency injection, middleware, configuration and clean, maintainable project structure.
3. Data and storage
- SQL: Strong skill in SQL, including joins, query optimization and working with Microsoft SQL Server.
- Vector databases: Hands-on experience with Azure AI Search, Pinecone or similar.
- Data pipelines: Prepare, clean and transform structured and unstructured data (PDFs, documents, logs) for AI workloads.
4. Communication
- Strong communication skills, including the ability to explain AI capabilities, limitations and trade-offs to technical and non-technical stakeholders.
Also required
- ML fundamentals: Understanding of embeddings, tokenization, fine-tuning, model selection and the basics of how transformers work.
- Cloud and MLOps: Design and deploy AI solutions on Azure (preferred), AWS or GCP. Experience with Azure AI Foundry or Azure ML, containerization (Docker, Kubernetes), CI/CD for AI services, and model and prompt versioning.
- Performance and scalability: Optimize AI systems for latency, throughput and token cost through caching, batching, concurrency and rate-limit handling.
- Queue management: Design message queues for reliable, asynchronous AI processing pipelines and communication between services.
- Collaboration: Work closely Product Owners, data teams, designers and front-end developers to turn business problems into AI solutions.
- Code review and mentoring: Review code, give constructive feedback, and mentor junior engineers on AI engineering best practices.
Must have
- 4+ years of software development experience, with 1+ years in AI/ML or LLM-based applications
- Strong skill in Python/Java/Php
- Hands-on experience with LLM APIs, prompt engineering and RAG architectures
- Experience with Azure cloud services, especially Azure OpenAI and Azure AI Search
- Proficiency in Microsoft SQL Server and at least one vector database
- Experience shipping AI features to production, not just prototypes
- Excellent verbal and written communication skills, with the ability to explain complex ideas clearly
Skills: Java,Python, LLMs, RAG, Prompt Engineering, Azure OpenAI, Vector Databases, SQL Server, Communication and Articulation
Good to have
- Experience with Angular or React for building AI-powered user interfaces, such as chat UIs and streaming responses
- Familiarity with WebSockets or Server-Sent Events for real-time AI responses
- Experience fine-tuning models or with open-source LLMs (Hugging Face, Ollama, vLLM)
- Knowledge of MCP (Model Context Protocol) and multi-agent systems
- Experience with RabbitMQ, Azure Service Bus or Kafka
Qualifications
- Bachelor’s or master’s degree in Computer Science, AI/ML, Data Science, Information Technology or a related field (or equivalent experience)
Skills
PythonJavaReactAngularFlaskFastAPIASP.NET.NETAWSAzureGCPDockerKubernetesCI/CDSQLSQL ServerData Science