Experience
6 - 10 yrs
Job Location
Hyderabad, India
Vacancy
1
Designation
Senior Engineer
Job Type
Not specified
Job Description
About the Role
We are looking for a highly experienced GenAI Engineer with deep expertise in prompt engineering, retrieval-augmented generation (RAG), and vector-based search systems to help integrate GenAI into our engineering and DevOps ecosystem.
As a contractor in this role, you will build intelligent, context-aware assistants and agents that augment CI/CD workflows, knowledge retrieval, incident handling, and developer productivity using LLMs, Python, and NLP pipelines. You will lead the charge in making our platform smarter and more autonomous by embedding GenAI natively into infrastructure and developer experience workflows.
Key Responsibilities
Design, iterate, and optimize prompts for task-specific LLM workflows with high accuracy, relevance, and controllability
Build RAG pipelines using vector databases like FAISS, Weaviate, Pinecone, or similar to support knowledge retrieval and contextual AI agents
Integrate LLMs into internal engineering tools, CI/CD workflows, and observability systems for real-time summarization, automation, and productivity enhancement
Leverage Python-based frameworks like LangChain, LlamaIndex, Haystack,and others to implement end-to-end GenAI solutions
Fine-tune open-source or commercial models (OpenAI, Claude, Cohere, Mistral, etc.) as needed for domain-specific use cases
Collaborate with DevOps and platform teams to identify automation and GenAI augmentation opportunities
Ensure data privacy, governance, and ethical standards are met when using internal datasets or embedding knowledge
Optimize response quality through prompt chaining, feedback loops, and context compression techniques
Experience integrating GenAI into CI/CD tools, observability dashboards, or chat-based interfaces
Required Skills Experience
6 10+ years in engineering roles with 2+ years hands-on in GenAI, NLP, or LLMbased systems
Proven experience in prompt engineering and LLM integration for backend or infrastructure automation
Strong Python development skills and working knowledge of LangChain, LlamaIndex, Hugging Face Transformers, or similar GenAI libraries
Deep understanding of vector databases (e.g., FAISS, Pinecone, Weaviate, Qdrant) and embedding techniques
Experience designing and deploying RAG architectures for internal knowledge access and contextually intelligent systems
Familiarity with NLP concepts like tokenization, summarization, intent extraction, semantic similarity, etc.
Experience with LLM APIs (OpenAI, Azure OpenAI, Claude, etc.) and streaming response generation
Ability to work in fast-paced, iterative development cycles in a collaborative DevOps/platform engineering environment
Nice to Have
Understanding of multi-modal models or fine-tuning open LLMs for specialized use
We are looking for a highly experienced GenAI Engineer with deep expertise in prompt engineering, retrieval-augmented generation (RAG), and vector-based search systems to help integrate GenAI into our engineering and DevOps ecosystem.
As a contractor in this role, you will build intelligent, context-aware assistants and agents that augment CI/CD workflows, knowledge retrieval, incident handling, and developer productivity using LLMs, Python, and NLP pipelines. You will lead the charge in making our platform smarter and more autonomous by embedding GenAI natively into infrastructure and developer experience workflows.
Key Responsibilities
Design, iterate, and optimize prompts for task-specific LLM workflows with high accuracy, relevance, and controllability
Build RAG pipelines using vector databases like FAISS, Weaviate, Pinecone, or similar to support knowledge retrieval and contextual AI agents
Integrate LLMs into internal engineering tools, CI/CD workflows, and observability systems for real-time summarization, automation, and productivity enhancement
Leverage Python-based frameworks like LangChain, LlamaIndex, Haystack,and others to implement end-to-end GenAI solutions
Fine-tune open-source or commercial models (OpenAI, Claude, Cohere, Mistral, etc.) as needed for domain-specific use cases
Collaborate with DevOps and platform teams to identify automation and GenAI augmentation opportunities
Ensure data privacy, governance, and ethical standards are met when using internal datasets or embedding knowledge
Optimize response quality through prompt chaining, feedback loops, and context compression techniques
Experience integrating GenAI into CI/CD tools, observability dashboards, or chat-based interfaces
Required Skills Experience
6 10+ years in engineering roles with 2+ years hands-on in GenAI, NLP, or LLMbased systems
Proven experience in prompt engineering and LLM integration for backend or infrastructure automation
Strong Python development skills and working knowledge of LangChain, LlamaIndex, Hugging Face Transformers, or similar GenAI libraries
Deep understanding of vector databases (e.g., FAISS, Pinecone, Weaviate, Qdrant) and embedding techniques
Experience designing and deploying RAG architectures for internal knowledge access and contextually intelligent systems
Familiarity with NLP concepts like tokenization, summarization, intent extraction, semantic similarity, etc.
Experience with LLM APIs (OpenAI, Azure OpenAI, Claude, etc.) and streaming response generation
Ability to work in fast-paced, iterative development cycles in a collaborative DevOps/platform engineering environment
Nice to Have
Understanding of multi-modal models or fine-tuning open LLMs for specialized use
Familiarity with developer-facing GenAI use cases: changelog generation, log triage, infra as code review, chatbot copilots
Experience working with Kubernetes, GitOps, or DevSecOps platforms a plus
Experience working with Kubernetes, GitOps, or DevSecOps platforms a plus
No Referrers Available
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