Job Description: 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.
- Clear 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.
