{"id":1953892,"url":"https://alion.io/job/prohance-ai-engineering-lead","title":"AI Engineering Lead","company":{"id":3843966,"name":"Prohance","domain":"prohance.ai","url":"https://alion.io/company/prohance-2","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":26000,"max_usd":63000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":32},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AutoGen","optional":false},{"name":"Claude","optional":false},{"name":"CrewAI","optional":false},{"name":"Function Calling","optional":false},{"name":"Gemini","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Tool Use","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"Fine-tuning","optional":true},{"name":"GCP","optional":true},{"name":"Kubernetes","optional":true},{"name":"LoRA","optional":true},{"name":"PEFT","optional":true},{"name":"QLoRA","optional":true},{"name":"Time Series Forecasting","optional":true}],"status":"live","first_seen_at":"2026-10-06T11:22:44Z","employer_posted_date":null,"last_verified_at":"2026-10-06T11:22:44Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"We are looking for an AI Engineering Lead to build and lead a team of 8-10 AI and full-stack engineers building the ProHance Agent, the agentic platform, and the intelligence layer that powers AI-first productivity governance products. This is a hands-on leadership role. You will work in the codebase, set technical direction, make architecture decisions, review critical work, and ship alongside your team while growing engineers, owning delivery, and raising the quality bar for how AI systems are built.\nYou will also help redefine how software is built in the wake of AI disruption. Engineering AI systems requires behavior to be specified, generated, evaluated, and refined rather than deterministically coded. The team is expected to serve as a lighthouse for the wider engineering organization by experimenting with new SDLC paradigms, proving what works, and codifying standards other teams can adopt. We are looking for a leader with a strong experimental mindset and a bias for execution, speed, and rapid iteration.\nResponsibilities:\nOwn the architecture of AI systems, including agentic workflows, multi-step reasoning, retrieval and grounding, tool use, and the LLM operations layer that keeps production AI healthy.\nMake key technical decisions on orchestration frameworks, model selection and routing, RAG design, and evaluation strategy.\nStay hands-on by contributing to architecture, reviewing critical code and designs, and leading by example in the codebase.\nEnsure AI systems integrate cleanly into the platform with the security, access control, and privacy handling required for enterprise workforce data.\nBuild, lead, and grow a high-performing team of 8-10 AI and full-stack engineers through hiring, mentoring, and development.\nOwn end-to-end delivery for the team's charter, from AI features and the ProHance Agent to the full-stack surfaces that expose them, balancing speed, quality, and reliability.\nSet clear technical direction and priorities, and create conditions for engineers to do their best work with high ownership and fast feedback loops.\nPioneer and operationalize new engineering paradigms for the AI era, including generator-evaluator loops, evaluation-driven development, intent expression, and loop engineering as first-class practices.\nBuild tooling and conventions that make AI system development repeatable, where behavior is specified, generated, evaluated, and refined.\nDrive adoption of AI-assisted engineering across the team and turn successful approaches into standards, playbooks, and practices for the wider engineering organization.\nAct as an internal reference point for how AI changes software development by evaluating emerging techniques and tools and influencing engineering leadership beyond the team.\nEstablish an operating model built for velocity, including rapid prototyping, tight ship-measure-learn cycles, and a willingness to stop what is not working.\nRun structured experiments to de-risk hard problems quickly and make evidence-based decisions on what to invest in and what to drop.\nProtect the team's ability to move fast without sacrificing the reliability and trust standards enterprise customers require.\nEstablish the quality bar and release criteria for AI features, including evaluation harnesses, golden datasets, and regression suites that catch issues before customers do.\nEnsure production AI systems meet enterprise standards for scalability, availability, observability, auditability, and explainability.\nBuild the trust architecture that allows enterprise buyers to interrogate and rely on AI outputs rather than treating the system as a black box.\nPartner with product, data science, and engineering leadership to translate strategy into an AI engineering roadmap and executable workflows.\nRepresent the team's technical direction to senior stakeholders and bring AI-native engineering thinking into broader organizational decisions.\nRequirements:\n6-10 years of experience in software or AI engineering, with at least 2 years leading and growing engineering teams.\nHands-on leadership experience with a track record of leading teams while staying technical, including coding and making architecture decisions.\nDemonstrable hands-on experience building and shipping production LLM or agentic AI systems, not just single-turn prompt-response applications.\nExperience with orchestration frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or equivalent.\nStrong understanding of RAG systems and evaluation discipline for AI applications.\nAbility to lead full-stack engineers credibly, with comfort across backend services, APIs, and modern application architecture.\nHands-on experience building against modern LLMs such as OpenAI GPT, Anthropic Claude, Google Gemini, or equivalent.\nStrong Python skills for AI pipeline and orchestration work, along with working proficiency across the full-stack ecosystem.\nProven history of shipping AI systems that work reliably in production, not just demos or prototypes.\nDemonstrated bias for action, experimentation, rapid iteration, and evidence-based decision-making under uncertainty.\nCuriosity and rigor in exploring AI-native engineering paradigms such as generator-evaluator loops, evaluation-driven development, and AI-assisted development practices, with the ability to codify them for others.\nNice To Have:\nExperience introducing AI-assisted engineering practices across a team or organization, with measurable adoption.\nExperience with enterprise-grade AI governance, including auditability, explainability, access control, and data privacy in regulated environments.\nFamiliarity with workforce analytics, operational telemetry, or time-series data as a reasoning domain.\nWorking knowledge of cloud platforms such as AWS, Azure, or GCP, along with Docker, Kubernetes, and CI/CD.\nUnderstanding of model routing, fine-tuning techniques such as LoRA and QLoRA, and inference optimization.\nContributions to open-source LLM tooling, published work, or demonstrated thought leadership on AI systems engineering or AI-native software development.","description_format":"text","description_chars":6079,"description_truncated":false,"requirements":{"experience_years_min":6,"management_years_min":2,"team_size_min":8,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Information Technology","Professional Services","Human Resources"],"lifecycle":[{"event":"open","at":"2026-10-06T11:22:44Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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