11 hours ago
Hyderābād, IndiaStaff+
Responsibilities
- Own enterprise AI architecture, reference architectures, reusable patterns, engineering guardrails, and technology decisions across complex engagements.
- Design and build production-scale generative AI, agentic AI, applied-ML, retrieval, and knowledge systems, including fine-tuning, RAG, semantic search, evaluation harnesses, and agent workflows.
- Architect multi-step and multi-agent systems with orchestration, state management, tool use, memory, human oversight, failure isolation, and verification controls.
- Engineer systems for latency, reliability, security, privacy, cost, scalability, maintainability, governance, monitoring, and continuous improvement.
- Define evaluation, observability, responsible AI, MLOps, and LLMOps standards, including quality metrics, telemetry, versioning, drift detection, rollback, and incident response.
- Design data and retrieval foundations covering ingestion, chunking, metadata, embeddings, vector and hybrid search, re-ranking, provenance, permissions, and content refresh.
- Advise client executives and engineering leaders, lead discovery and technical workshops, shape roadmaps and architectures, provide estimates, and support pre-sales solutioning.
- Guide initiatives from problem framing and experimentation through production rollout, operational readiness, adoption, and measurable business value.
- Raise engineering capability through architecture reviews, design documents, code reviews, mentoring, reusable accelerators, and technical standards.
Requirements
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.
- 15+ years of progressive experience across software engineering, machine learning, data, or AI, including substantial ownership of production systems at scale.
- Principal-level technical leadership with architecture ownership, cross-team technical direction, and decision-making across complex engagements or product portfolios.
- Proven experience deploying generative AI, agentic AI, and/or applied-ML products into production beyond proofs of concept.
- Deep hands-on expertise with Python, modern LLM and ML architectures, agent frameworks, Google Cloud and Vertex AI, production APIs, and scalable software systems.
- Experience establishing evaluation, observability, security, privacy, governance, and responsible AI controls for enterprise AI solutions.
- Strong data engineering and SQL knowledge, with the ability to design reliable data and retrieval foundations for AI products.
- Senior client-facing experience communicating technical choices, risks, and business value to executive stakeholders and engineering teams.
- Preferred experience training, tuning, or adapting neural networks, embedding models, or foundation models.
- Preferred architecture experience in healthcare, financial services, public-sector, regulated, or other high-stakes domains.
- Google Cloud Professional Machine Learning Engineer, Professional Cloud Architect, or comparable certification is preferred.
- Preferred experience building reusable AI accelerators, reference implementations, or platform capabilities across clients or product teams.
- Preferred pre-sales experience with discovery workshops, solution shaping, estimation, proposal development, and technical presentations.
- Familiarity with Kubernetes, infrastructure as code, event-driven architectures, and modern data platforms is preferred.
Tech Stack
Categories
About Egen
Egen is a privately held IT services firm that designs and builds cloud, data, analytics, AI, and platform solutions for enterprises and public-sector clients. It delivers consulting, engineering, and managed services across major ecosystems, including Google Cloud and Salesforce. Founded in 2000 and headquartered in Naperville, Illinois, Egen operates across North America, Europe, and India.