Technical Architect (Python + AI)
Technical Architect is a hands on AI/GenAI architect and delivery lead responsible for designing, building, and taking production grade conversational and agentic AI systems to market. This role owns end to end solution architecture from business problem framing through reference implementation, deployment, and operational readiness while providing strong technical leadership to delivery teams.
This is not a pure advisory role. The architect is expected to actively design, prototype, review code, and unblock teams in complex, ambiguous GenAI engagements. The ideal candidate will collaborate with cross-functional teams to deliver innovative solutions, ensuring alignment with organisational goals and industry best practices.
Key Responsibilities
- Lead Brand Concierge engagements in the capacity of an Architect cum Lead , taking full ownership of Technical Delivery and customer success .
- Lead the design and architecture of AI-driven platforms, ensuring scalability, security, and performance.
- Collaborate with stakeholders to understand business requirements and translate them into winning solutions.
- Evaluate and select appropriate AI frameworks, tools, and technologies for various use cases.
- R emain hands on during early and complex phases of delivery as required .
- Oversee the development, deployment, and integration of machine learning models and AI services.
- Establish best practices for AI development, including data governance, model management, and MLOps processes.
- Design and implement LLM based and Agentic AI applications using modern orchestration frameworks.
- Select and apply appropriate LLMs , embedding strategies, retrieval patterns, and tool calling approaches based on use case requirements.
- Own non functional requirements including latency, cost optimization, observability, and failure handling.
- Mentor and guide technical teams in the adoption and implementation of AI technologies.
- Collaborate with engineering teams to solve complex customer issues with innovative solutions.
- Ensure compliance with regulatory requirements, data privacy, and ethical AI standards.
- Prepare technical documentation, architectural diagrams, and reports as required .
Qualifications and Skills
- Bachelor s or Master s degree in Computer Science , Engineering, Information Technology, or a related field.
- 10 + years of experience in software architecture, with at least 3 years in AI/ML solution design and implementation
- Strong proficiency in one or more programming languages (Python, Java, R, etc.) relevant to AI development including Front End technologies , with strong proficiency in Python, SQL, and Javascript
- Hands-on experience with AI/ML frameworks such as TensorFlow, PyTorch , Keras , or Scikit-learn.
- Strong Expertise in cloud platforms (Azure, AWS, GCP) and containerisation (Docker, Kubernetes).
- Excellent understanding of data engineering, model deployment, and MLOps pipelines.
- Strong understanding of Agentic AI application architecture and implementation
- Strong analytical, problem-solving, and communication skills.
- Ability to work collaboratively in a multicultural and distributed team environment.
- Relevant AI certifications (e.g., Microsoft Certified: Azure AI Engineer Associate, Google Professional Machine Learning Engineer , AI/ML post grad programs from reputed institutes, etc. ).
- Travel to customer location s domestically or internationally if there is a requirement , especially during key milestones .
Preferred Skills
- Experience with deep learning architectures (CNNs, RNNs, LSTMs, Transformers).
- Familiarity with MLOps and CI/CD pipelines for model deployment and monitoring.
- Understanding of ethical AI practices and data privacy regulations.
- Published research papers or contributions to open-source AI projects.
- LLM: Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral , Llama
- LLM Ops: ML Flow, Langchain , Langraph , LangFlow , Flowise , LLamaIndex , SageMaker, AWS Bedrock, Vertex AI, Azure AI
- Databases/Datawarehouse: DynamoDB, Cosmos, MongoDB, RDS, MySQL, PostGreSQL , Aurora, Spanner, Google BigQuery .
- Dev Ops (Knowledge): Kubernetes, Docker, FluentD , Kibana, Grafana, Prometheus
- Cloud Certifications (Bonus): AWS Professional Solution Architect, AWS Machine Learning Specialty, Azure Solutions Architect Expert
- Stay updated with the latest advancements in AI, machine learning, and data science, and recommend relevant innovations.
