Engineering Manager

Engineering Manager Steelbazaar

Location: Noida, On-site
Reports to: Head of Engineering
Team: 3–6 AI/ML engineers (direct reports)
Employment type: Full-time

About Steelbazaar

Steelbazaar is a B2B steel marketplace connecting buyers and sellers across the steel value chain, bringing transparency, efficiency, and technology to how steel is bought, sold, and delivered. We're building the data and AI backbone that powers pricing intelligence, matching, and automation across the platform.

About the role

We're looking for a hands-on AI Engineering Manager to build and lead a small, high-impact team responsible for Steelbazaar's AI/ML capabilities end to end — from marketplace intelligence (pricing, demand-supply matching, search and recommendations, credit/fraud risk scoring) to GenAI-powered products (RFQ and document parsing, AI-assisted quoting, customer support copilots) and, where relevant, computer vision for material and quality inspection. This role owns the AI platform broadly: the data foundations, MLOps infrastructure, and the applied AI teams that ship models into production.

You'll split your time between people leadership, technical architecture, and staying close enough to the code and models to make good calls with your team.

What you'll do

• Lead, mentor, and grow a team of 3–6 AI/ML engineers, setting technical direction, running performance reviews, and hiring as the team scales.
• Own the AI/ML roadmap across marketplace intelligence (pricing forecasting, demand-supply matching, search/recommendations, fraud and credit risk models), GenAI products (chatbots, RFQ/document parsing, AI-assisted quoting, internal copilots), and computer vision use cases (material and quality inspection, inventory/logistics automation) as priorities dictate.
• Design and oversee the data platform and MLOps infrastructure needed to build, deploy, monitor, and retrain models reliably in production — feature stores, pipelines, model registries, CI/CD for ML, observability.
• Partner closely with Product, Data Engineering, and business stakeholders (sales, operations, supply chain) to translate business problems into AI solutions and prioritize the roadmap.
• Stay hands-on: review architecture and code, get into notebooks or production issues when needed, and set engineering standards for model development, evaluation, and deployment.
• Own build-vs-buy decisions for AI tooling and vendor/LLM provider evaluations (OpenAI, Anthropic, open-source models, etc.).
• Establish practices for responsible AI — data privacy, model bias/fairness checks, and human-in-the-loop safeguards where relevant.
• Report on AI initiative outcomes and ROI to leadership, and communicate technical tradeoffs to non-technical stakeholders.

What we're looking for

• 10+ years in software/ML engineering, including 5+ years directly managing or leading an engineering/ML team.
• Strong hands-on background in applied ML/AI — you've shipped models (classical ML, deep learning, and/or LLM-based systems) to production, not just in notebooks.
• Solid understanding of MLOps practices: data pipelines, model versioning, monitoring, and CI/CD for ML systems.
• Experience with at least two of: recommendation/ranking systems, time-series forecasting, NLP/LLM applications, computer vision, or risk/fraud scoring models.
• Practical experience integrating LLMs into products (prompt engineering, RAG, fine-tuning, or agentic workflows) is a strong plus given our GenAI roadmap.
• Comfortable working with cloud infrastructure (AWS/GCP/Azure) and modern data stacks (Spark, Airflow, dbt, or similar).
• Proven ability to balance technical depth with people management — you can review a model architecture and also run a 1:1 well.
• Strong communication skills; able to work directly with business and product teams in a fast-moving marketplace environment.
• Prior experience in B2B marketplaces, e-commerce, fintech, or industrial/manufacturing domains is a plus, but not required.
• Bachelor's or Master's degree in Computer Science, Data Science, or a related field (or equivalent practical experience).

What we offer

• Ownership of AI strategy and execution in a fast-growing B2B marketplace.
• A small, focused team where your technical decisions have direct, visible business impact.
• Competitive compensation, [benefits — health insurance, ESOPs, etc. as applicable].