Lead AI Engineer

About the role: You will be part of the AI team responsible for building next-generation AI capabilities at Helpshift. The team works on various projects, including AI Agents for automated issue resolution, User Intent Detection, AI-Powered Answers and Knowledge Base Enhancements, Agent CoPilot for agent productivity, and LLM-based systems, proprietary ML models, and multimodal RAG pipelines.

About the team: The team includes Backend Engineers, Full-stack Developers, ML Engineers, Data Scientists, and Frontend Developers—collaborating to deliver intelligent and scalable AI experiences.

Responsibilities:

• Lead and mentor a team of backend and frontend developers, fostering a high-performing, collaborative engineering culture.
• Own the technical direction, architecture, and strategy for LLM, AI Agent, and ML initiatives.
• Architect and build AI Agents, RAG pipelines (including multimodal RAG), LLM-based services, and evaluation frameworks.
• Design, build, and deploy scalable AI/ML systems from concept to production.
• Collaborate closely with PMs, ML Engineers, and Data Scientists to convert business requirements into AI-driven solutions.
• Oversee full SDLC for your squad—ensuring strong engineering practices around coding, testing, quality, and reliability.
• Conduct detailed technical reviews and provide guidance to maintain code quality.
• Troubleshoot performance, scalability, and reliability issues in production AI systems.
• Champion continuous improvement, automation, and innovation within the team.
• Keep the team up-to-date with advances in LLMs, LLMOps, fine-tuning techniques, vector search, and generative AI.

Requirements:

• 8–10 years of experience in software development.
• More than 5 years of hands-on experience in AI/ML.
• More than 2 years in an engineering leadership role, preferably leading a cross-functional team.
• Hands-on experience building:
• AI Agents / Agentic workflows
• LLM pipelines (inference, orchestration, guardrails)
• RAG systems (vector search, knowledge bases, multimodal RAG)
• LLM fine-tuning (SFT, LoRA, instruction tuning)
• LLMOps workflows (monitoring, evaluation, optimization, versioning)
• Strong understanding of ML algorithms, embeddings, transformers, vector semantics, and distributed systems.
• Experience with Vector Databases such as Elasticsearch, Weaviate, Pinecone, or Milvus.
• Proficiency with ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Experience with cloud platforms (AWS, GCP, Azure) and MLOps tooling.

Good to have:

• Solid foundation in backend technologies such as Python, Java, Go, Node.js, and experience with Kafka.
• Knowledge of the Gaming industry or Customer Support domain.
• Familiarity with frontend frameworks (React, Angular, Vue.js).
• Exposure to functional programming (Clojure preferred; our team uses Clojure).
• Experience in startup/high-growth environments.
• Open-source contributions in AI/ML/LLM projects.
• Experience with containerization and orchestration (Docker, Kubernetes).

Bonus Points:

• Experience building production-grade LLM-powered automation for customer support or gaming.
• Publications, presentations, or contributions related to LLMs, RAG, or AI Agents.

Benefits:

• Hybrid setup
• Worker's insurance
• Paid Time Offs
• Other employee benefits to be discussed by our Talent Acquisition team in India.