Associate Architect - Data Science
About the role
The Associate Architect – Data Science will be responsible for designing and guiding scalable enterprise AI and ML solutions, including Machine Learning, Generative AI, and Agentic AI. The role involves defining AI architecture and standards, supporting MLOps/LLMOps, evaluating emerging technologies, and collaborating with engineering, data, security, and business teams to deliver secure, production-ready AI solutions. Key responsibilities include establishing architectural standards, leading the design of AI platforms, overseeing the AI/ML ecosystem, ensuring compliance with governance frameworks, and mentoring technical staff. The position requires deep technical expertise in AI/ML, experience with cloud platforms, and strong leadership and communication skills.
Must have
- Bachelor's degree in IT, Computer Science, Software Engineering, Data Science, Engineering, Mathematics, or a related field
- 8–10 years of professional experience in Data Science, AI, or ML, working in production-grade environments, with significant experience in solution architecture and enterprise-scale system design
- Deep expertise in Machine Learning and Deep Learning, including advanced model design, optimization, and large-scale deployment
- Extensive hands-on and architectural experience in Generative AI (LLMs, RAG pipelines, embeddings, fine-tuning, evaluation frameworks)
- Strong experience designing Agentic AI systems (multi-agent architectures, orchestration frameworks, tool ecosystems, autonomous decision-making)
- Proven track record in implementing MLOps practices at scale (CI/CD for ML, automated pipelines, monitoring, retraining strategies)
- Advanced expertise in LLMOps, including prompt lifecycle management, evaluation pipelines, guardrails, observability, latency, and cost optimization
- Strong programming skills in Python and deep familiarity with AI/ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn)
- Experience with cloud platforms (AWS, Azure, or GCP) and cloud?native platforms & services (e.g., Copilot Studio, Bedrock, Vertex AI, Azure OpenAI)
- Strong understanding of data engineering and data platform architecture (ETL/ELT pipelines, feature stores, data lakes/warehouses)
- Experience with distributed systems, microservices, APIs, and event driven architectures in AI contexts
- Strong system thinking and ability to design end-to-end enterprise AI architectures
- Proven leadership in guiding multiple teams and influencing senior stakeholders
- Experience defining and enforcing architecture governance, standards, and best practices
- Excellent communication and stakeholder management skills, including C-level engagement
- Strong focus on scalability, reliability, security, and cost-efficiency in AI systems
- Ability to balance innovation with practical, production-grade delivery
Technologies
- Agentic AI
- AWS
- Azure
- CI/CD
- Data Engineering
- Data Lake
- Data Pipelines
- Data Science
- Deep Learning
- ELT
- Embeddings
- ETL
- Event-Driven Architecture
- Fine-Tuning
- GCP
- Generative AI
- LLM
- LLMOps
- Machine Learning
- Microservices
- Microsoft Copilot
- MLOps
- Observability
- OpenAI
- Python
- PyTorch
- RAG
- Responsible AI
- scikit-learn
- TensorFlow
- Vertex AI
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