ProHance OverviewProHance is a fast-growing B2B SaaS company focused on helping enterprises gain a clear, data-driven view of workforce productivity and operational effectiveness. Trusted by global enterprises across industries, ProHance enables leaders to make better decisions by combining deep work analytics with actionable insights.

As enterprises rapidly adopt AI and automation, ProHance is at the forefront of this shift — building AI-powered, insight-driven products that help organizations measure impact, improve productivity, and realize ROI from technology investments. Central to this vision is the concept of eGDP: the idea that productive hours, work output, AI usage, and vendor effort can be converted into a single, defensible measure of enterprise economic activity.

We are now building the AI layer of the ProHance platform — including the intelligence engine, the ProHance Agent, and the measurement framework for human + AI productivity governance. This role leads the team that makes that real.

Job PositionTitle: AI Engineering Lead

Location: Bangalore

Experience: 6–10 years, with at least 2 years in an engineering management / team-lead role

Education: B.Tech / M.Tech in Computer Science or a related engineering field

Reporting to: Senior Vice President

Role Type: Player-Coach — hands-on technical leadership + people management

Role OverviewWe are looking for an AI Engineering Lead to build and lead the team at the heart of ProHance's AI transformation — a group of 8–10 AI and full-stack engineers building the ProHance Agent, the agentic platform, and the intelligence layer that powers our AI-first productivity governance products.

This is a hands-on leadership role. You will still be in the codebase — setting technical direction, making architecture calls, reviewing critical work, and shipping alongside your team — while growing engineers, owning delivery, and raising the quality bar for how AI gets built here.

Just as importantly, you will help redefine how software is built in the wake of AI disruption. Engineering an AI system is a different discipline from traditional software — behaviour is specified, generated, evaluated, and refined rather than deterministically coded. Your team is expected to be the lighthouse for the wider ProHance engineering organization: experimenting with new SDLC paradigms, proving what works, and codifying it into standards other teams adopt.

We want a leader with a genuine experimental mindset and a strong bias for executing at speed and iterating quickly — someone who ships, measures, learns, and moves, rather than over-planning.

Key ResponsibilitiesHands-On AI Engineering & Architecture• Own the architecture of ProHance's AI systems — agentic workflows, multi-step reasoning, retrieval and grounding, tool use, and the LLM operations layer that keeps production AI healthy

- Make the hard technical calls on orchestration frameworks, model selection and routing, RAG design, and evaluation strategy
- Stay hands-on — contribute to architecture, review critical code and designs, and lead by example in the codebase
- Ensure AI systems integrate cleanly into the ProHance platform with the security, access control, and privacy handling enterprise workforce data demands

Team Leadership & Delivery• Build, lead,



and grow a high-performing team of 8–10 AI and full-stack engineers — hiring, mentoring, and developing engineers across both disciplines
- Own end-to-end delivery for the team's charter — from AI features and the ProHance Agent through to the full-stack surfaces that expose them — balancing speed, quality, and reliability
- Set clear technical direction and priorities, and create the conditions for engineers to do their best work with high ownership and fast feedback loops

Redefining the SDLC — AI-Native Engineering (Lighthouse Mandate)• Pioneer and operationalise current engineering paradigms for the AI era — generator–evaluator loops, evaluation-driven development, intent expression, and loop engineering as first-class practices
- Treat AI system development as a discipline where behaviour is specified, generated, evaluated, and refined — not deterministically coded — and build the tooling and conventions that make this repeatable
- Drive adoption of AI-assisted engineering across your team, and turn what works into standards, playbooks, and practices the wider ProHance engineering organization can adopt
- Act as the internal reference point for how AI changes the way software is built — evaluating emerging techniques and tools, and influencing engineering leadership beyond your own team

Speed, Experimentation & Iteration• Establish an operating model built for velocity — rapid prototyping, tight ship-measure-learn cycles, and a willingness to kill what isn't working
- Run structured experiments to de-risk hard problems quickly, and make evidence-based calls on what to invest in and what to drop
- Protect the team's ability to move fast without sacrificing the reliability and trust standards enterprise customers require

Quality, Reliability & Governance• Establish the quality bar and release criteria for AI features — evaluation harnesses, golden datasets, and regression suites that catch issues before customers do
- Ensure production AI systems meet enterprise standards for scalability, availability, observability, auditability, and explainability
- Build the trust architecture that lets enterprise buyers interrogate and rely on AI outputs — the system is not a black box

Cross-functional Collaboration• Partner with product, data science, and engineering leadership to translate strategy into an AI engineering roadmap and executable workflows
- Represent the team's technical direction to senior stakeholders, and bring AI-native engineering thinking into wider organizational decisions

Required Qualifications• Experience: 6–10 years in software / AI engineering, with at least 2 years leading and growing engineering teams

- Hands-on leadership: A track record of leading teams while staying technical — you have not stopped writing code or making architecture decisions
- AI depth:



Demonstrable hands-on experience building and shipping production LLM / agentic AI systems — not just single-turn prompt-response apps — including orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen, or equivalent), RAG, and evaluation discipline
- Full-stack fluency: Ability to lead full-stack engineers credibly — comfortable across backend services, APIs, and modern application architecture
- LLM providers: Hands-on experience building against modern LLMs (OpenAI GPT, Anthropic Claude, Google Gemini, or equivalent)
- Programming: Strong Python for AI pipeline and orchestration work; working proficiency across the full-stack ecosystem
- Production mindset: Proven history of shipping AI systems that work reliably in production — not demos or prototypes
- Experimentation mindset & speed: Demonstrated bias for action — running experiments, iterating quickly, and making evidence-based calls under uncertainty
- SDLC innovation: Genuine curiosity and rigour in exploring AI-native engineering paradigms — generator–evaluator loops, evaluation-driven development, and AI-assisted development practices — and the ability to codify them for others

Preferred Qualifications• Experience introducing AI-assisted engineering practices across a team or organization, with measurable adoption
- Experience with enterprise-grade AI governance — auditability, explainability, access control, and data privacy in regulated environments
- Familiarity with workforce analytics, operational telemetry, or time-series data as a reasoning domain
- Working knowledge of cloud platforms (AWS, Azure, or GCP), containerisation and orchestration (Docker, Kubernetes), and CI/CD
- Understanding of model routing, fine-tuning (LoRA, QLoRA), and inference optimisation
- Contributions to open-source LLM tooling, published work, or demonstrated thought leadership on AI systems engineering or AI-native software development

What Success Looks Like in This Role• A high-performing AI + full-stack team is in place, delivering reliably and growing in capability
- The ProHance Agent and AI features ship and operate reliably in production — accurate, trusted, and governed to enterprise standards
- AI-native engineering practices take hold — the generator–evaluator loops and AI-assisted development your team pioneers become standards other teams adopt
- The team is recognised across ProHance as the reference point for how AI changes the way software is built
- Speed and quality coexist — the team ships fast without eroding the trust and reliability enterprise customers demand
- Engineers on the team grow, take on greater ownership, and describe this as the place they did their best work

Why Join Us• Lead the team at the centre of ProHance's AI transformation — with direct impact on how global enterprises govern their workforces in an AI-augmented world
- Stay hands-on while you lead — this is a builder's leadership role, not a step away from the craft
- Own a genuinely hard problem: reasoning over heterogeneous enterprise telemetry with the reliability and auditability CFOs and COOs demand
- Define how AI-native software gets built — and set the standard the rest of the organization follows
- High ownership from day one, working alongside product, data science, and engineering leaders who think rigorously about the problem

📌 AI Engineering Lead (Bengaluru)
🏢 ProHance
📍 Bengaluru