Lead the technical vision for AI and machine learning initiatives by transforming business needs into robust, future-ready architectures. As a Principal Engineer and Technical Architect, you will own the end-to-end design of complex systems, ensuring alignment with functional goals, scalability demands, and security standards.
Key Responsibilities
- Interpret client objectives and translate them into clear, efficient technical blueprints that guide development teams.
- Evaluate multiple architectural approaches and recommend optimal solutions based on performance, cost, and long-term maintainability.
- Establish benchmarks for non-functional requirements including latency, security, and system resilience.
- Author and review comprehensive design documentation covering system architecture, integration patterns, and technology choices.
- Conduct rigorous evaluations of system designs for scalability, extensibility, and adherence to best practices in AI/ML engineering.
- Design full-stack solutions that integrate AI components, data pipelines, and cloud infrastructure to meet both functional and operational needs.
- Lead proof-of-concept initiatives to validate technology choices and architectural assumptions before full-scale implementation.
- Diagnose and resolve complex technical issues through root cause analysis and structured problem-solving.
- Mentor engineering teams on AI integration, coding standards, and system design principles.
Required Expertise
- Over 13 years of experience in software and systems engineering with a focus on AI/ML solutions.
- Proven background in designing and deploying machine learning systems in NLP, computer vision, and generative AI.
- Strong command of AI architecture on cloud platforms, including data orchestration, model deployment, and inference optimization.
- Proficiency in Python, Java, or .NET, with hands-on experience in data processing libraries such as Pandas and NumPy.
- Deep knowledge of transformer models, LLMs, prompt engineering, and retrieval-augmented generation (RAG).
- Experience with fine-tuning techniques including PEFT, LoRA, and Q-LoRA.
- Familiarity with multi-agent systems and autonomous decision-making frameworks.
- Working knowledge of MLOps tools like MLflow, Kubeflow, Docker, and Kubernetes for model lifecycle management.
- Understanding of database technologies including SQL, MySQL, and Oracle.
- Commitment to ethical AI, data privacy, and secure system design.
- Strong analytical and communication skills, with the ability to collaborate across technical and business domains.
