Tech Specialist - AI & Full-stack Systems

Location: Pune, India

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.

1. 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.

1. 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.

1. 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.

1. 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.