Sr. Consultant, Software Engineer - India

Role summary:

The Solutions Engineer/Architect – Emerging Technology R&D Lab is accountable for shaping and delivering end‑to‑end technology solutions for Horizon 2/3 use cases. They:

• Lead a squad of R&D engineers (AI/ML, data, app, infra) exploring emerging tech.

• Bridge Emerging Tech with infrastructure, operations, architecture, product, security, and business partners.

• Own the backlog, technical direction, and path‑to‑production for lab initiatives, from idea through PoC, pilot, and handoff.

• You will focus primarily on providing high quality, efficient technology solutions to business partners by crafting new software applications or modifying and/or supporting existing packaged or custom-built applications. In this capacity, you'll code, configure, test, debug, document and maintain applications.

Key responsibilities:

• Partner with product and business leaders to define R&D objectives, use‑case charters, and success criteria.

• Maintain and prioritize the R&D backlog (experiments, PoCs, platform enablers), aligning with emerging technology big rocks and tech strategy.

• Translate business needs into solution options and target architectures for emerging tech (e.g., Gen AI, Physical AI, world models, agentic patterns).

• Own end‑to‑end technical design for lab solutions, from data and models through APIs, UX, monitoring, and controls.

• Ensure lab solutions can graduate into production platforms (patterns, reference implementations, handoff artifacts).

• Act as primary interface across infrastructure, architecture, product and business, extended teams such as data, security, compliance, legal and vendor partners

• Facilitate design reviews and technical decisions, documenting trade‑offs and recommendations.

• Coordinate day‑to‑day execution of the R&D backlog: scope experiments, size work, define milestones and exit criteria (PoC → pilot → scale or stop).

• Ensure secure software and systems engineering practices across the lifecycle (data protection, model risk controls, access, logging).

• Remove blockers for the team (environments, tools, data access, approvals).

• Coach engineers on solution thinking (not just model or feature building) and how to design for eventual scale.

• Promote reuse (patterns, components, accelerators) and share learnings with other teams.

• Ensure lab work aligns with AI risk, compliance, and security policies; proactively engage risk partners as needed.

• Assess technical feasibility, scalability, and operational readiness of solutions before recommending production.

• Prepare and deliver concise, outcome‑focused updates to leadership: progress, findings, value, risks, and next steps.

• Translate complex emerging‑tech concepts into business‑relevant narratives for non‑technical stakeholders.

MUST HAVE:

Leadership: Demonstrated ability to lead and guide a team of engineers in AI solutioning and development.

Architecture: AI/ML/ Gen AI / Agentic AI / Physical AI (Robotics)

Languages: Python

Cloud & Deployment - Experienced with two Cloud Platforms: AWS/Azure/GCP

Datastores: PostgreSQL, DynamoDB

Integration: Open API / Swagger/Apigee

Observability & SRE: Open Telemetry, Splunk, New Relic; Honey Hive or similar,

Security & Compliance: OWASP Top10 remediation, secrets management, policy-as-code (OPA / Sentinel)

NICE to HAVES:

Architecture: Agentic, RAG, Domain-Driven Design, Event-Driven (Kafka / SNS-SQS), Nice to have - Data Lake, Analytics understanding

Languages: Node.js, Java (Spring Boot)

Cloud & Deployment: Several Cloud platforms AWS/Azure/GCP Kubernetes (EKS), CDK, Pod man Compose for local

Datastores: Several Oracle Integration: Graph QL Federation, Async API

Observability & SRE: Open Telemetry, Splunk, New Relic; Honey Hive or similar Security & Compliance: OWASP Top10 remediation, secrets management, policy-as-code (OPA / Sentinel).