GenAI Engineer

Additional Important Note For Applicants

• Currently, only immediate joiners (who have already completed their notice period) or candidates serving a notice period of up to 30 days will be considered for this opportunity.
• Candidates with longer notice periods may not be considered at this stage due to urgent project requirements.
  
  

Important Note for Applicants

Kindly read the job description carefully before applying. Please apply only if your experience, technical skills, and notice period align with the mandatory requirements mentioned above. Profiles that do not meet the core criteria may face rejection during the screening process, which can lead to unnecessary time and effort from both sides. We appreciate your understanding and cooperation.

Job Title: GenAI Engineer

Experience

4.5-6 Years

Location:

Pune (Viman Nagar)

Shift Timings

11:00 AM – 8:00 PM

Job Overview

We are looking for an experienced Senior AI Engineer / Player-Coach to lead the development of next-generation Generative AI solutions focused on intelligent search, content automation, and AI-driven business workflows.

This is a highly hands-on leadership role requiring strong expertise in LLM application development, Retrieval-Augmented Generation (RAG) systems, MLOps, and cloud-native AI infrastructure. The ideal candidate should be capable of architecting scalable AI systems while mentoring a small engineering team and collaborating closely with product and business stakeholders.

Key Responsibilities

• Design, develop, and deploy advanced Retrieval-Augmented Generation (RAG) systems for intelligent search and discovery platforms
• Build and optimize generative AI solutions for content creation, summarization, metadata enrichment, and workflow automation
• Evaluate, implement, and manage Gen AI infrastructure using managed LLM services or self-hosted GPU-based environments
• Define and implement best practices for:
• Repository structure
• CI/CD pipelines
• Prompt management
• Model versioning
• Implement observability and monitoring for AI systems using tools such as OpenTelemetry and Prometheus
• Monitor and optimize AI performance metrics including latency, cost, accuracy, hallucination detection, toxicity monitoring, and data drift
• Lead and mentor a team of AI/ML engineers
• Collaborate with product, engineering, and leadership teams to deliver scalable AI-driven solutions
• Drive experimentation, rapid prototyping, and continuous improvement across AI initiatives
  

Required Skills & ExperienceCore Experience

• 4+ years of hands-on Python development experience
• Strong experience building and productionizing LLM-powered applications
• Experience leading technical teams or mentoring engineers
  
  

Must-Have Technical SkillsGenerative AI & LLM Frameworks

Hands-on Experience With

• DSPy
• LangChain
• LlamaIndex
• Hugging Face Transformers
  
  

RAG Systems & Vector Databases

Strong Expertise In

• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• Chunking strategies
• Embedding pipelines
• Vector databases such as:
• Pinecone
• Weaviate
• Milvus
  

Cloud & Infrastructure

Strong Cloud Experience With

• Azure OpenAI Service or AWS Bedrock
• Azure or AWS infrastructure services
• Kubernetes orchestration (AKS/EKS)
• Serverless services
• Cloud storage solutions
  
  

MLOps & DevOps

Experience With

• Docker
• CI/CD pipelines
• GitHub Actions
• Argo Workflows
• Model registries
• AI deployment best practices
  
  

Leadership & Communication

• Proven ability to lead small, highly technical teams
• Strong stakeholder communication and collaboration skills
• Ability to translate business requirements into scalable AI solutions
  
  

Good-to-Have Skills

• Experience with agentic AI workflows:
• AutoGen
• CrewAI
• Familiarity with multi-modal AI models (text, image, etc.)
• Experience with advanced fine-tuning techniques:
• LoRA
• QLoRA
• Strong SQL skills
• Experience with ClickHouse
• Exposure to inference cost optimization techniques

Skills: aws infrastructure services,rag systems,chunking strategies,langchain,dspy,model registries,github actions,argo workflows,kubernetes orchestration,embedding pipelines,mlops,llamaindex,weaviate,hugging face transformers,prompt engineering,pinecone,ci/cd pipelines,azure openai service,docker,milvus,leadership