Specialist II - ML Engineering

• Solution Architecture & Delivery: Architect, design, and deliver robust, scalable GenAI solutions across discovery, development, regulatory, and commercial domains; own end-to-end delivery and production health (performance, reliability, security).
• Reference Architecture & Reuse: Define and maintain enterprise reference architectures, reusable components, patterns, and best practices that accelerate GenAI adoption and reduce time-to-value.
• Business Partnership & Value Creation: Work closely with stakeholders to identify and prioritize high‑value GenAI use cases; translate business needs into practical technical solutions that balance innovation and feasibility.
• Governance, Compliance & Responsible AI: Ensure solutions embed responsible AI principles (transparency, fairness, privacy, security); implement model/data governance, lifecycle standards, and regulatory compliance controls.
• Innovation & Ecosystem Leadership: Evaluate and pilot emerging LLMs, frameworks, and orchestration tools (for example OpenAI, Anthropic, Llama, LangChain, LlamaIndex); cultivate partnerships with vendors, startups, and academia.
• Proofs-of-Concept & Adoption: Lead POCs and pilots, demonstrate business impact, and champion adoption of effective GenAI capabilities across the organization.
• Technical Leadership & Capability Building: Provide hands‑on guidance and mentorship to engineering teams; develop training, documentation, and communities of practice to grow long‑term GenAI capability across the India hub and global teams.
• Operational Readiness: Define monitoring, incident response, cost controls, and SRE/ops practices for GenAI services in multi‑cloud environments.

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or related field.
• Experience: 13+ years in enterprise IT, solution architecture, or engineering leadership with multiple AI/ML/GenAI delivery projects.
• Technical Expertise: Deep hands‑on experience with LLMs and agent architectures, vector databases, RAG/Retrieval pipelines, and multi‑agent systems.
• Cloud & Deployment: Proven ability to design and deliver solutions across multi‑cloud platforms (e.g., Azure AI Foundry, AWS AI services, Google/Vertex/Gemini ecosystem).
• Delivery & Governance: Strong track record of delivering production systems with attention to security, compliance, model lifecycle management, and data privacy.
• Communication & Leadership: Excellent stakeholder management, influencing skills, and ability to lead cross‑functional teams.

• Research & Tools: Familiarity with LangChain, LlamaIndex, DSPy or similar orchestration/LLM tooling; experience evaluating LLM model families and cost/performance trade-offs.
• Ecosystem Experience: Established partnerships or experience collaborating with AI vendors, startups, or academic research groups.
• Operationalization: Experience building deployment pipelines, model monitoring, drift detection, and automated retraining workflows.

• Impact: Clear, measurable business outcomes from GenAI initiatives (reduced manual effort, improved decision speed, measurable ROI).
• Adoption: Reusable architecture and components are adopted across teams and reduce time-to-production for new GenAI use cases.
• Governance: Model and data governance processes are in place, audited, and demonstrably reduce risk exposure.

UST