3986b7f9-290 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.

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]]> full-time senior hybrid AI/ML, LLM, RAG, Vector Databases, ML frameworks, Cloud platforms, MLOps tooling, Python, Java, Go, Node.js, Kafka, React, Angular, Vue.js, Clojure, Docker, Kubernetes Engineering Technology Helpshift https://logos.yubhub.co/j.com.png Helpshift is a software company that provides customer support solutions. It has a team of engineers and developers working on various projects. https://apply.workable.com https://apply.workable.com/j/91E59A0696 Pune, Maharashtra, India 2026-03-09 d0a30328-204 Lead AI Engineer Compensation\n\n$250K – $300K • Offers Equity\n\n## ABOUT SPHERE\n\nEvery breakthrough in trade infrastructure has followed the same pattern: reduce a transaction cost, expand the market. Containerization for goods. SWIFT for money. Stripe for payments. Compliance is one of the last and largest — and the hardest, because trade rules aren't data to be looked up. They're a complex adaptive system with 190+ sovereign jurisdictions, in different languages, changing constantly, reacting to each other.\n\n## THE ROLE\n\nYou'll lead development of TRAM, our proprietary AI reasoning model that reads and interprets global trade law. This isn't a lookup problem, it's a reasoning problem — and it only became solvable with LLMs. You'll build the data pipelines that ingest legal sources, the model stack that produces structured evidence, the evaluation frameworks that measure accuracy, and the fine-tuning loops that improve performance. The unusual constraint: you need speed, scale, correctness, and robustness simultaneously — at millisecond latency, zero downtime, heading toward billions of transactions where a single error costs a customer $20K.\n\n## WHAT YOU'LL DO\n\n_Within weeks:_\n\n- Lead development of new features aimed at increasing TRAM’s test-time accuracy\n\n- Work on the underlying data and retrieval pipelines that help power our AI workflows\n\n- Work directly with our internal tax experts to understand how TRAM can better reason like them\n\n_Within months:_\n\n- Own TRAM’s eval framework and workflows\n\n- Work directly with leading frontier labs to reinforce fine tune models on our proprietary data\n\n## REQUIREMENTS\n\n- Prior experience building AI enabled products, particularly RAG systems\n\n- Experience fine tuning base models, ideally via RFT\n\n- Willingness to dive into tax technical problems - if you aren’t willing to dive deep on how the model should reason through the tax research process you won’t be effective\n\n- A strong understanding of how LLMs and reasoning models function\n\n## NICE TO HAVES\n\n- Experience working with LLMs on legal applications\n\n- Experience with RAG data pipelines and collecting/curating data for the pipeline\n\n## WHO YOU ARE\n\n_You'll thrive here if:_\n\n- You're a Dog. You've been underestimated, gone through struggle, and never stopped running. You have a chip on your shoulder and enormous drive. You look at Stripe, Deel, and Flexport all punting on compliance and think: _good, that means the opportunity is ours._ Hunger beats pedigree.\n\n- Early stage is in your bones. You've built things where there's no playbook and nobody handing you the answer. You define the problem instead of waiting for instructions.\n\n- You own it end to end. Give you a goal and you figure out your own path. Small team, global surface area — everyone owns a domain that would be a full team at a larger company. No one tells you how.\n\n- You believe speed and accuracy are both possible. We're building a complex product that requires robustness and 100% uptime, and we have to build at our customers' pace. Move fast. Don't break things. Both.\n\n- Being in the room is a feature, not a cost. Five days in SF isn't a policy, it's how the work gets done. The speed and density of collaboration we need doesn't survive over video.\n\n_This won't be a fit if:_\n\n- You need structure handed to you or ambiguity feels draining rather than motivating\n\n- You want to manage people more than own hard problems (we're a flat, experienced team — everyone builds)\n\n- You're used to "good enough" shipping (small errors have outsized impact here)\n\n- Being in the room five days a week feels like a cost instead of a benefit

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]]> Full time senior On-site $250K – $300K • Offers Equity AI, RAG systems, LLMs, Reasoning models, Data pipelines, rarity, Experience working with LLMs on legal applications, Experience with RAG data pipelines and collecting/curating data for the pipeline Engineering Technology Sphere https://logos.yubhub.co/sphere.com.png Sphere built a system that solves global trade compliance by ingesting trade law, interpreting it, and producing compliance determinations more reliable than human experts. https://jobs.ashbyhq.com https://jobs.ashbyhq.com/sphere/4e3c5943-bd07-4ce1-8e13-68b00221d0b7 San Francisco HQ 2026-03-08