About the Role:
The Bridge Builder We are seeking a visionary and hands-on AI Architect to lead a mission-critical new team responsible for bridging the gap between pioneering academic research and robust, enterprise-grade AI solutions.
This is not a purely academic or purely production roleit's the dynamic center where they meet. You will be the technical force driving the transformation of brilliant prototypes into scalable, secure, and performant products that solve real-world problems for our clients.
If you thrive on solving complex, ambiguous technical challenges that require both deep AI expertise and modern software architecture leadership and are passionate about mentoring and guiding a high-calibre team, this is your opportunity to build the next generation of core capabilities for our clients.
Key Responsibilities:
Define, Design, Deliver I. Technical & Architectural Leadership (The 'Define & Design' Phase)
Pioneer the Transformation:
Lead the end-to-end evolution of high-potential AI/ML research projects and prototypes into scalable, reliable, production-ready Minimum Viable Products (MVPs). Strategic Architecture:
Design and implement robust system architectures that skillfully balance speed-to-market, production quality, security, and technical debt in a fast-paced environment. Decision Catalyst:
Be the technical authority and align with senior technical counterparts for decision making as and when required, making critical, data-driven design and technology choices (e.g., model serving, MLOps stack, deployment patterns). Best Practices Champion:
Establish and enforce world-class standards for code quality, testing, deployment, and operational excellence. Cross-Geography Collaboration
: Serve as the technical bridge between India engineers and US engineers. Synthesize feedback, escalate blockers, and ensure alignment without sacrificing velocity.
II. AI/ML Expertise & Deployment (The 'Deliver' Phase)
Production Deployment:
Architect, deploy, and optimize complex AI/ML models, including cutting-edge Large Language Models (LLMs) and large-scale neural networks, across diverse, multi-domain production environments. System Operations (MLOps):
Implement sophisticated DevOps/MLOps practices covering experiment tracking, model versioning, continuous integration/delivery (CI/CD), and performance monitoring. Optimization:
Relentlessly optimize models and systems to meet real-world production constraints like low latency, cost efficiency, and accuracy trade-offs. Cutting-Edge Knowledge:
Proactively research and integrate the latest advancements in LLMs, foundation models, and emerging machine learning techniques.
III. Team Leadership & Mentorship
Cultivate Excellence:
Directly lead, mentor, and technically guide a small, high-impact team of 3-5+ engineers. Grow the Team:
Actively participate in the hiring and interviewing process to build out the engineering talent. Foster Collaboration:
Champion a culture of technical innovation, continuous improvement, and seamless knowledge sharing between research, product, and engineering teams.
IV. Required Qualifications
Technical Leadership
- Proven experience deploying AI/ML products to production across multiple domains.
- Deep understanding of LLMs, Prompt Models, and large-scale neural network architectures.
- Expertise in designing fusion prompt models and statistical models for diverse applications.
- Strong foundational experience in traditional machine learning algorithms and techniques.
- Proficiency in Python for both research and production code.
- Hands-on experience with AWS (EC2, S3, Lambda, DynamoDB, SageMaker, EKS/ECS, etc.) and strong knowledge of containerization (Docker/Kubernetes).
- Experience with system architecture design and microservices.
- Strong knowledge of database design (SQL and NoSQL).
- Familiarity with API design (RESTful or GraphQL).
Leadership & Process Acumen
- Minimum 2-3 years leading or mentoring technical teams of 3+ members.
- Demonstrated ability to convert concepts into practical product constraints and make tough build/buy/scope decisions.
- Expertise leading technical product life cycles from concept to delivery.
- Experience with agile development methodologies (Scrum/Kanban).
- Strong decision-making skills in ambiguous situations.
Preferred Qualifications
- Experience in end-to-end model building, deployment, and performance monitoring.
- Experience with real-time systems or streaming data.
- Experience with model compression, optimization techniques, or model serving frameworks.
- First-author publication in Q1 SCI journal or top-tier CS conference OR experience as a reviewer for academic engineering/CS journals.
- Open-source contributions or technical blog writing.
Key Competencies
- Bridge Builder: Translate between academic research and production engineering
- Fast Executor: Rapid prototyping and iteration in uncertain environments
- Technical Depth: Deep AI/ML knowledge combined with strong engineering fundamentals
- Pragmatic Decision Maker: Balance perfectionism with shipping velocity
- Collaborative Leader: Work effectively across research, engineering, and product teams
- Continuous Learner: Stay ahead of rapidly evolving AI/ML landscape
