Specialist - Software Engineering
LTM · Bengaluru · onsite · Posted 29d ago
Not disclosed6–8 yrsfulltimeSourced from
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About this role
Role Description
Role Snapshot
Primary Focus
AI product engineeringCORE STACK
Python FastAPIFlaskAI EXPERTISE
LLMs RAGDELIVERY
Production ownership
Also looking at P2 with 4 5 Years of Experience
Role Overview
We are seeking a handson Senior AI Software Engineer to design build deploy and operate productiongrade Generative AI capabilities This role is ideal for an engineer who combines strong backend fundamentals with practical experience building solutions spanning Generative AI RAG and Agentic AI
Key Responsibilities
Design and develop Python services that integrate with leading LLM APIs including OpenAI Anthropic or Amazon Bedrock
Build endtoend RAG pipelines covering document ingestion chunking embeddings vector search retrieval prompt construction and response generation
Build agentic AI capabilities incorporating tool execution workflow orchestration memory guardrails and MCPbased integrations
Implement LLMOps practices including observability telemetry performance monitoring and cost optimisation
Develop robust REST APIs and backend services using FastAPI or Flask
Improve response quality by diagnosing prompt behavior retrieval relevance hallucinations context limitations and token constraints
Engineer production resilience through retries ratelimit handling error management observability and operational support
Deploy AIenabled features to cloud environments and take ownership of reliability maintainability and performance
Collaborate with product architecture engineering and quality teams to translate use cases into secure usable and testable solutions
Contribute to code reviews engineering standards technical documentation and continuous improvement of the AI delivery platform
Leverage AI pair programming tools GitHub Copilot Cursor AI Gemini Code Assist to accelerate development code conversion and version upgrades
Required Qualifications
6 years of professional experience in Python backend or software engineering
At least 1 year of handson experience delivering LLMenabled features or applications to production
Experience with agent frameworks such as Microsoft Agent Framework MAF LangChainLangGraph AutoGen or CrewAI
Demonstrated experience building RAG pipelines using embeddings vector databases retrieval strategies and prompt orchestration
Strong proficiency in Python and practical experience with FastAPI or Flask
Production experience integrating LLM APIs such as OpenAI Anthropic or Amazon Bedrock
Practical understanding of prompt debugging contextwindow constraints token management hallucination mitigation and model response evaluation
A track record of shipping userfacing features and supporting them in production
Strong problemsolving debugging communication and crossfunctional collaboration skills
Preferred Qualifications
Experience with LangChain or LlamaIndex
Experience with vector databases such as Pinecone Weaviate or ChromaDB
Experience creating rapid prototypes or demonstrations using Streamlit
Cloud deployment experience on AWS Microsoft Azure or Google Cloud Platform
Familiarity with CICD containerization monitoring security controls and automated testing for AI services
What Success Looks Like
AI features are delivered as working maintainable product capabilities not just prototypes
Services remain reliable under production traffic rate limits failures and changing model behavior
Response quality improves through systematic evaluation retrieval tuning prompt refinement and defect resolution
Code is clear testable documented and aligned with engineering standards
IDEAL CANDIDATE A handson engineer who enjoys building debugging and operating AI features with a strong bias toward working software and measurable outcomes
Role Snapshot
Primary Focus
AI product engineeringCORE STACK
Python FastAPIFlaskAI EXPERTISE
LLMs RAGDELIVERY
Production ownership
Also looking at P2 with 4 5 Years of Experience
Role Overview
We are seeking a handson Senior AI Software Engineer to design build deploy and operate productiongrade Generative AI capabilities This role is ideal for an engineer who combines strong backend fundamentals with practical experience building solutions spanning Generative AI RAG and Agentic AI
Key Responsibilities
Design and develop Python services that integrate with leading LLM APIs including OpenAI Anthropic or Amazon Bedrock
Build endtoend RAG pipelines covering document ingestion chunking embeddings vector search retrieval prompt construction and response generation
Build agentic AI capabilities incorporating tool execution workflow orchestration memory guardrails and MCPbased integrations
Implement LLMOps practices including observability telemetry performance monitoring and cost optimisation
Develop robust REST APIs and backend services using FastAPI or Flask
Improve response quality by diagnosing prompt behavior retrieval relevance hallucinations context limitations and token constraints
Engineer production resilience through retries ratelimit handling error management observability and operational support
Deploy AIenabled features to cloud environments and take ownership of reliability maintainability and performance
Collaborate with product architecture engineering and quality teams to translate use cases into secure usable and testable solutions
Contribute to code reviews engineering standards technical documentation and continuous improvement of the AI delivery platform
Leverage AI pair programming tools GitHub Copilot Cursor AI Gemini Code Assist to accelerate development code conversion and version upgrades
Required Qualifications
6 years of professional experience in Python backend or software engineering
At least 1 year of handson experience delivering LLMenabled features or applications to production
Experience with agent frameworks such as Microsoft Agent Framework MAF LangChainLangGraph AutoGen or CrewAI
Demonstrated experience building RAG pipelines using embeddings vector databases retrieval strategies and prompt orchestration
Strong proficiency in Python and practical experience with FastAPI or Flask
Production experience integrating LLM APIs such as OpenAI Anthropic or Amazon Bedrock
Practical understanding of prompt debugging contextwindow constraints token management hallucination mitigation and model response evaluation
A track record of shipping userfacing features and supporting them in production
Strong problemsolving debugging communication and crossfunctional collaboration skills
Preferred Qualifications
Experience with LangChain or LlamaIndex
Experience with vector databases such as Pinecone Weaviate or ChromaDB
Experience creating rapid prototypes or demonstrations using Streamlit
Cloud deployment experience on AWS Microsoft Azure or Google Cloud Platform
Familiarity with CICD containerization monitoring security controls and automated testing for AI services
What Success Looks Like
AI features are delivered as working maintainable product capabilities not just prototypes
Services remain reliable under production traffic rate limits failures and changing model behavior
Response quality improves through systematic evaluation retrieval tuning prompt refinement and defect resolution
Code is clear testable documented and aligned with engineering standards
IDEAL CANDIDATE A handson engineer who enjoys building debugging and operating AI features with a strong bias toward working software and measurable outcomes
Sourced from foundit
Skills
Dockermodel response evaluationRetrieval Augmented GenerationBedrockAutoGenStreamlitCrewaiautomated testinggoogle cloud platformamazon web servicesMonitoringsecurity controlsvector databasesEmbeddingsLarge Language Models (LLMs)OpenAI SDKFlask Frameworkretrieval strategiesPrompt orchestrationPythontoken managementhallucination mitigationMicrosoft AzureLanggraphLangchainCi/CdFastAPIAnthropic SDKprompt debugging