
AI Integration Engineer
Leapfrog Technology · Pune, Mahārāshtra, India · Hybrid
Weak hiring record · median 74 days open
Hiring signal
Actively hiring
Posted 5 days ago · still listed on the employer's own board · How we score
All reasons
- 60% of this employer's openings are over 60 days old
In its favour
- Posted 5 days ago
- Still listed on the employer's own board
The AI Integration Engineer builds the connective tissue between LLM APIs, existing product systems, and end users. They are not training models. They are integrating, orchestrating, and deploying AI capabilities into production software, reliably, securely, and at scale.
Key Responsibilities
- Integrate LLM APIs (Anthropic Claude, OpenAI GPT-4o, AWS Bedrock, Google
Gemini) into backend services and user-facing products
Design and implement RAG pipelines
document ingestion, chunking strategy,
vector store selection, retrieval tuning
- Build agentic workflows using frameworks such as AgentCore, LangChain,
LlamaIndex, or custom orchestration patterns
- Manage prompt engineering, prompt versioning, and prompt evaluation
frameworks
Implement guardrails for LLM outputs
validation, content filtering, fallback logic
Monitor AI system performance
latency, cost-per-query, accuracy drift, token
usage
- Collaborate with frontend engineers to surface AI capabilities in product UIs
- Own the AI integration layer in the SDLC, from spec to CI/CD to production
observability
Education and Experience
- A degree (Masters preferred) in Computer Science, Engineering, Artificial Intelligence, Data Science, Applied Mathematics, or related fields.
- Minimum 5+ years of hands-on experience in engineering.
Technical Skills
- 4+ years backend or full stack experience; strong API design and consumption skills
- Proven experience integrating LLM APIs (any major provider) into production applications, not just prototypes
- Proficiency in Python and/or TypeScript/Node.js
- Hands-on experience with RAG vector databases Pinecone, Weaviate, pgvector etc), embedding models, chunking strategies.
- Understanding of prompt engineering: system prompts, few-shot examples, chain-of-thought, structured output
- Solid grasp of API security, rate limiting, cost management for LLM-based services
- Experience with AWS or another major cloud platform
- Desirable Skills:
- Experience with agentic frameworks: AgentCore, LangChain, LlamaIndex,
CrewAI, AutoGen, or similar
- Familiarity with multi-modal AI (vision, audio) or function calling / tool use
- Understanding of fine-tuning workflows even if not hands-on
- Experience using AI coding assistants to accelerate their own development workflow
Soft Skills
- Highly motivated, self-driven, entrepreneurial mindset, and capable of solving complex analytical problems under high-pressure situations.
- Ability to work with cross-functional and cross-regional teams.
- Ability to lead a team of Data/AI professionals and work with senior management, technological experts, and the product team.
- Excellent written and verbal communication skills, comfortable with client communication.
- Good leadership skills - ability to motivate and mentor team members, ability to plan and make sound decisions, ability to negotiate tactfully with the client and team.
- Results-oriented, customer-focused with a passion for resolving tough technical and operational challenges.
- Possess excellent analytical and problem-solving abilities.
- Good documentation skills.
- Experienced with Agile methodologies like Scrum/Kanban
Skills
- LLM
- Anthropic Claude
- OpenAI
- Gpt-4o
- Bedrock
- Gemini
- Retrieval-Augmented Generation
- LangChain
- LlamaIndex
- Prompt Engineering
- Python
- TypeScript
- Node.js
- Vector Databases
- Pinecone
- Weaviate
- pgvector
- AWS
- CrewAI
- AutoGen
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