Technical Architect (Agentic AI)
On-siteBengaluru, Karnataka, India
Job Summary
Lead the design and architecture of AI-driven platforms, ensuring scalability, security, and performance while collaborating with stakeholders to translate business requirements into innovative solutions. Evaluate and select appropriate AI frameworks and tools, then oversee the development, deployment, and integration of machine learning models and AI services. Establish best practices for data governance, model management, and MLOps processes, and mentor technical teams in adopting emerging AI technologies. Ensure compliance with regulatory requirements, data privacy, and ethical AI standards while preparing technical documentation and architectural diagrams. Stay updated with the latest advancements in AI, machine learning, and data science to recommend relevant innovations.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related field
- 10+ years of experience in software architecture
- 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
- Hands-on experience with AI/ML frameworks such as TensorFlow, PyTorch, Keras, or Scikit-learn
- Excellent understanding of data engineering, model deployment, and MLOps pipelines
- Strong analytical, problem-solving, and communication skills
- Ability to work collaboratively in a multicultural and distributed team environment
- Proficient in Python
- Proficient in SQL
- Proficient in Javascript
Desired Qualifications
- A PhD is an added advantage
- Expertise in cloud platforms (Azure, AWS, GCP) and containerisation (Docker, Kubernetes) is highly desirable
- Relevant AI certifications (e.g., Microsoft Certified: Azure AI Engineer Associate, Google Professional Machine Learning Engineer)
- Experience in developing AI solutions for Indian or international markets
- Demonstrated ability to manage multiple projects and priorities effectively
- 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
- Experience with 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
- Cloud Knowledge: AWS/Azure/GCP
- Dev Ops (Knowledge): Kubernetes, Docker, FluentD, Kibana, Grafana, Prometheus
- Cloud Certifications (Bonus): AWS Professional Solution Architect, AWS Machine Learning Specialty, Azure Solutions Architect Expert
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