Walk-in || Direct Walk In -AI Engineer @09 Sep 26 - Bangalore

Role & responsibilities

• Bachelors degree in Computer Science, Engineering, or a related technical field; advanced degree a plus. • 8-10 years of overall software engineering experience, with at least 3 years focused on AI/LLM application development and 1+ years designing multi-agent or complex agentic systems in production. • 3+ years leading engineering teams or squads in a technical lead capacity, with demonstrated ownership of both technical direction and delivery outcomes for a team of 4-5 or more engineers. • Hands-on experience running Scrum in a software engineering team: sprint planning, backlog refinement, estimation, stand-ups, reviews, and retrospectives, with a track record of predictable sprint delivery. • Demonstrated ability to write clear user stories and acceptance criteria, manage a technical backlog, and forecast delivery using velocity and capacity data. • Proven ability to design and deliver end-to-end technical systems from data and infrastructure through application logic to monitoring and operations. • Deep expertise in Python and strong proficiency in at least one additional language (TypeScript/Node.js, Java, or Go) used in backend or integration contexts. • Advanced experience with agentic frameworks: LangGraph, CrewAI, AutoGen, AWS Bedrock Agents or custom orchestration, including multi-agent coordination, state management, and tool-use patterns. • Production-grade experience with RAG systems at scale: advanced retrieval strategies, hybrid search, re ranking pipelines, evaluation, and knowledge base maintenance. • Hands-on infrastructure engineering experience: Terraform or AWS CDK, CI/CD pipeline design, container orchestration (ECS or EKS), and IAM/security configuration on AWS. • Experience designing and operating distributed backend systems: event-driven architectures, async processing, API design, and service integration patterns. • Strong track record of production observability: structured logging, distributed tracing, metrics, alerting, and cost management for cloud-native AI workloads. • Deep, hands-on experience with AI-assisted coding tools (Cursor, Claude Code, Amazon Kiro, or similar), including the ability to design, document, and govern team-wide coding workflows that leverage these tools, evaluate new entrants in the space, and drive adoption best practices across the engineering team. • Proficiency with agile delivery and collaboration tooling such as Jira, Monday.com, Azure DevOps, or similar. • Demonstrated ability to mentor engineers and lead technical design discussions with diverse stakeholders. • Strong written and verbal communication skills, including the ability to report delivery status and risk to non-technical stakeholders.

Preferred candidate profile

• Certified Scrum Master (CSM), Professional Scrum Master (PSM), SAFe, or equivalent agile certification. • Experience operating within a PMO governance model, including structured status reporting, risk registers, and change management practices. • Experience coordinating delivery across cross-functional, matrixed, offshore, or multi-vendor teams. • Experience with AWS Quick Suite or similar enterprise AI platforms. • Experience designing AI platforms or internal developer tooling that enables other engineers to build and deploy agents more efficiently. • Familiarity with Model Context Protocol (MCP), agent interoperability standards, and tool-use specifications. • Experience with fine-tuning, RLHF, or adapting open-source models for domain-specific enterprise tasks. • Knowledge of data platform engineering: Snowflake, Databricks, DBT, or similar for managing the data layer that feeds AI systems. • Experience with AI governance frameworks, responsible AI practices, or ML model risk management in regulated or enterprise environments. • Experience in manufacturing, supply chain, or industrial environments. • Exposure to frontend or UI engineering sufficient to design thin interfaces, dashboards, or operator tooling that complements AI back-end systems