Forward Deploy AI Engineer

Were looking for an experienced Data Scientist to join our Supply Chain AI team in India. In this role, youll design, fine-tune, and deploy optimized Small Language Models (SLMs) that power production-grade AI features across logistics, planning, inventory, and demand forecasting.

What Makes This Role Unique

This is a product-first data science role. You wont just build modelsyoull own how they show up in real products used by large enterprises. Your work becomes the intelligence layer that sits on top of our graph and indexing systems and directly drives customer outcomes.

What Youll Do

• Build and fine-tune SLMs/LLMs for production use cases such as semantic search, forecasting, contract intelligence, and conversational insights. Optimize models for performance, cost, and latency using PEFT, quantization, and efficient inference techniques

• Embed supply chain knowledge into models by working closely with domain experts and product teams

• Enable agentic workflows, allowing AI Assistants to execute planning, optimization, and decision-support tasks

• Integrate models into products using custom Lambdas, and a Graph-based microservices architecture

• Apply vector search and semantic reasoning to help customers navigate complex supply chain relationships

• Work with real enterprise dataERP, logistics, inventory, supplier, and contract datasets

• Deploy and operate models using strong MLOps practices on platforms like AWS, GCP or AZURE

• Measure and improve quality, reducing hallucinations and improving reliability through continuous evaluation

• Partner with engineering, product, and operations teams to ship impactful AI features end-to-end

• Experience working with SLMs (Mistral, Phi, Nemotron, Llama variants), applying parameter-efficient fine-tuning (LoRA/QLoRA) to optimize task-specific natural language query answering with high accuracy and minimized hallucinations.

• Strong understanding of supply chain data and workflows (logistics, inventory, procurement, ERP systems)

• Familiarity with Knowledge Graphs and graph-based data modeling, including designing and querying Neo4j graphs for relationship-driven insights.

• Solid hands-on skills in Python, PyTorch/TensorFlow, GraphQL, and vector databases

• 8+ years of experience in forecasting, optimization, supply chain analytics, or AI-driven products

• Experience designing Retrieval-Augmented Generation pipelines using document chunking, metadata filtering, hybrid (dense + sparse) retrieval, and SLM/LLM grounding for enterprise knowledge search.

• Hands-on experience building and tuning FAISS-based vector indexes (IVF, HNSW, PQ) for large-scale semantic similarity search with low-latency production deployment

• Experience taking models from prototype to production

• Bonus: reinforcement learning, MARL, graph neural networks, or probabilistic modeling

• Clear communication skills and a strong sense of product ownership

Avathon is the only physical AI unicorn headquartered in the San Francisco Bay Area, we go beyond models and dashboards we deploy AI that continuously computes across supply chains, energy systems, and industrial operations. Our Operational Technology platform turns fragmented data into real-time, autonomous decisioning across the systems that power the global economy. This is not digital AI. This is AI for operational reality where latency, constraints, and failure have real-world consequences. Cutting-Edge AI Innovation Join a team at the forefront of AI, developing groundbreaking solutions that shape the future. High-Growth Environment Thrive in a fast-scaling startup where agility, collaboration, and rapid professional growth are the norm. Meaningful Impact Work on AI-driven projects that drive real change across industries and improve lives. Learn more at: Avathon Staff Data Scientist Supply Chain AI (Small Language Models) At Avathon, we build AI products that solve real industrial problems at scale. We believe the future of enterprise AI lies in small, focused, high-performance modelsdeeply grounded in domain contextnot just large, generic systems.