: Tech Specialist - AI &

- Full-stack Systems

- Role: Senior Individual Contributor (IC)

- Reporting to: Management

- Location: Pune, Maharashtra

- Experience: 6–10 years

About Us: The Source Journey

At Source, we are digitizing the backbone of India's economy by streamlining traditional supply chains through technology and lean operations.

- 2014 | The Spark: Mission to digitize India's fragmented industrial supply chains.

- 2017 | The Pivot to Lean: Focused on zero user behavior change and a profits-first mindset.

- 2020 | Digital Breakthrough: First Indian B2B firm with a WhatsApp API license; grew through COVID disruptions.

- 2022 | Scaling New Heights: Raised $1M from India SME Investments, scaling to ₹1,000 Cr topline profitably.

- 2025 | The 10x Era: Series A expansion into Recycled Polymers and Paints; moving 0.5M tons of material across India.

- 2026 &
- Beyond | The Mission Continues:

Scaling to 1M tons, defining the future of Indian industrial distribution.

About the Role

We are looking for a hands-on engineer to own complex technology initiatives from problem discovery to production-grade deployment and business outcomes.

This is a senior individual contributor (IC) role focused on engineering depth, problem-solving, and architectural ownership—not team management. You will work directly with business stakeholders, design architectures, write production code, and build AI-native systems end-to-end.

Key Responsibilities

1. End-to-End Problem Ownership

- Own complex technology initiatives across the full lifecycle: problem discovery, solution design, deployment, and post-launch outcomes.

- Engage directly with business stakeholders to clarify ambiguous requirements, define actual problem statements, and align on scope.

- Remain accountable for total business impact rather than just code delivery.

2. Architecture and Hands-on Engineering

- Design and build scalable, reliable, secure, and maintainable software systems.

- Drive technical choices, evaluate tradeoffs,



and write production-grade code across backend services, APIs, and data pipelines.

- Take projects from initial POCs to production deployment and long-term maintenance.

3. AI and LLM Engineering

- Design, train, evaluate, and deploy Large Language Models (LLMs), including training models from scratch.

- Build AI-native solutions that directly address core business workflows.

- Handle the end-to-end model lifecycle: data prep, architecture, fine-tuning/RAG, inference optimization, latency/cost control, and production monitoring.

4. Delivery &
- Production Ownership

- Deliver high-quality software on clear timelines by breaking complex tasks into milestones and proactively managing risks.

- Own testing, CI/CD, deployment, monitoring, incident response, and continuous evaluation for traditional and AI software.

5.

Technical

Judgment &

- Improvement

- Maintain high engineering standards and document key architectural decisions.

- Continuously optimize systems to reduce complexity, lower operational costs, and improve system observability and developer productivity.

Eligibility Criteria

- Education: Bachelor's degree in CS, Computer Engineering, IT, or related technical field. (Master's in CS/AI/ML preferred; equivalent strong industry experience accepted).

- Experience: 6–10 years of hands-on software engineering experience delivering complex systems to production.

Required Technical Expertise

- Backend Engineering: Proficiency in Node.js, TypeScript/JavaScript, API design, async processing, and event-driven architecture.

- System Design: Expertise in distributed systems, scalability, database design, caching, messaging,



and system integration.

- AI/ML &
- LLM Engineering:

Deep understanding of deep learning, transformer architectures, tokenization, attention mechanisms, GPU utilization, and LLM internals.

- LLM Training: Direct experience training LLMs from scratch, fine-tuning, RAG, and serving models in production.

- Frameworks &

- Languages:

Python, PyTorch, or TensorFlow.

- Data Engineering: Vector embeddings, data pipelines, dataset curation, and processing.

- Infrastructure &

- Ops:

Docker, CI/CD, cloud/on-prem deployment, monitoring, and logging.

- Databases: SQL/NoSQL schema design, query optimization, and performance tuning.

- Engineering Best Practices: Automated testing, code reviews, version control, and security best practices.

Profile &

- Mindset

- Owner Mindset: Takes full responsibility for outcomes without needing every step defined.

- Deep Problem Solver: Investigates business root causes and edge cases before designing software.

- Hands-on Craftsperson: Writes code, debugs complex issues, and builds maintainable systems.

- Transparent Communicator: Explains technical concepts clearly to business stakeholders to drive consensus.

- Pragmatic: Makes smart trade-offs between speed, cost, maintainability, and scalability.

Role Scope &
- Boundaries

- NOT a people-management or admin position.

- NOT a project coordination or task-tracking role.

- NOT limited to prompt engineering or AI experimentation—solutions must run reliably in production.

Success Metrics

- Successful end-to-end execution of complex engineering initiatives.

- Measurable business impact and system ROI.

- System quality, stability, scalability, and maintainability.

- Successful deployment and operation of production AI/LLM models.

- On-time delivery with effective risk management.

> The ideal candidate is a builder, problem solver, and owner who can translate complex business problems into production-ready software.

📌 Tech Specialist - AI & Full-stack Systems (Pune)
🏢 Source.one
📍 Pune