AI Technical Lead

InApp; Ernakulam; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

InApp in India seeks an experienced AI Technical Lead to drive the design, development, and deployment of enterprise AI solutions. You will provide technical leadership and hands-on execution to cross-functional teams in a hybrid model.

The role combines architecture, development, mentoring, and delivery ownership to ensure scalable, secure, and business-aligned AI initiatives, including RAG-based workflows and integration with enterprise systems.

Qualifications

• 7–12 years in AI/ML solution development.
• Experience with production AI deployments.
• Strong leadership and mentorship.

Responsibilities

• Technical Leadership: Define and review AI solution architectures and implementation approaches.
• Hands-on Development: Design and develop AI-powered applications and workflows.
• Mentoring & Team Enablement: Provide technical mentorship and conduct code/architecture reviews.
• Governance & Quality: Define evaluation frameworks and ensure security, privacy, and responsible AI practices.

Skills

OpenAI

Anthropic Claude

Gemini

Prompt engineering

RAG

LangChain

Vector databases

Embeddings

Python

REST APIs

Microservices

AWS

Job description

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AI Technical Lead

Experience : 7+ years Project Location(s) : Trivandrum / Kochi (Hybrid ) , Remote

NP : Immediate joiners or 15days notice period

Number of Openings: 1

Job Description

We are seeking an experienced AI Technical Lead to drive the design, development, and implementation of enterprise-grade AI solutions. The ideal candidate will possess strong hands-on experience building and deploying AI applications in production environments and will provide both technical leadership and execution support to development teams.

This role requires a balance of solution architecture, hands-on development, mentoring, and delivery ownership to ensure AI initiatives are scalable, secure, reliable, and aligned with business objectives.

7–12 Years (with minimum 3+ years in AI/ML and Generative AI solution development)

Key Responsibilities

Technical Leadership

• Define and review AI solution architectures and implementation approaches.
• Guide engineering teams on best practices for AI application development.
• Identify technical risks, implementation gaps, and optimization opportunities.
• Ensure AI solutions meet performance, scalability, security, and maintainability requirements.

Hands-on Development

• Design and develop AI-powered applications and workflows.
• Build and optimize Retrieval-Augmented Generation (RAG) solutions.
• Develop AI agents, orchestration workflows, and automation solutions.
• Integrate LLMs with enterprise applications, APIs, databases, and third-party systems.
• Support deployment, testing, monitoring, and troubleshooting of AI solutions in production environments.

Mentoring & Team Enablement

• Provide technical mentorship to developers and junior AI engineers.
• Conduct code reviews and architecture reviews.
• Establish development standards, reusable frameworks, and implementation guidelines.
• Help teams translate business requirements into effective AI solutions.

Governance & Quality

• Define evaluation frameworks and success metrics for AI solutions.
• Implement observability, monitoring, and performance tracking.
• Ensure compliance with security, privacy, and responsible AI practices.
• Drive continuous improvement through experimentation and adoption of emerging AI technologies.

Required Skills

Generative AI & LLMs

• Strong experience working with OpenAI, Anthropic Claude, Gemini, or similar LLMs.
• Expertise in prompt engineering and prompt optimization.
• Experience building RAG-based applications.
• Knowledge of AI agent frameworks and orchestration patterns.
• Understanding of model evaluation, grounding, hallucination mitigation, and AI quality assurance.

AI Engineering

• Experience with LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar frameworks.
• Experience integrating vector databases such as Pinecone, Weaviate, Chroma, or Qdrant.
• Knowledge of embeddings, semantic search, and retrieval techniques.
• Experience implementing AI workflows and automation solutions.

Software Engineering

• Strong programming skills in Python.
• Experience with REST APIs and microservice architectures.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Understanding of CI/CD, deployment pipelines, and DevOps practices.
• Experience working with SQL and NoSQL databases.

Production Experience

• Proven experience delivering AI solutions to production environments.
• Experience monitoring and optimizing AI application performance.
• Strong troubleshooting and debugging capabilities for AI systems at scale.

Preferred Qualifications

• Experience leading AI teams or mentoring engineers.
• Experience building enterprise AI assistants, copilots, chatbots, or workflow automation solutions.
• Familiarity with MLOps and AI governance practices.
• Exposure to manufacturing, ERP, inventory, or field service domains is a plus.

Key Success Criteria

• Successful delivery of production-ready AI solutions.
• Improved development velocity through technical leadership and mentoring.
• Reduced implementation rework and technical debt.
• Establishment of scalable AI engineering practices and standards.
• High-quality, maintainable, and secure AI implementations.