- We are seeking an experienced AI Architect to lead the design, development, and implementation of enterprise-scale AI and Machine Learning solutions.

- The ideal candidate will define AI architecture strategies, guide technical teams, and ensure scalable, secure, and production-ready AI systems that deliver measurable business value.

- This role demands deep hands-on expertise in AI/ML engineering, Generative AI, and Large Language Model (LLM) ecosystems, combined with strong architectural leadership and cross-functional collaboration.

Key Responsibilities :

AI & ML Architecture :

- Define and own enterprise AI architecture covering data ingestion, model training, deployment, monitoring, and governance.

- Design scalable and resilient ML pipelines for batch and real-time inference.

- Establish reference architectures and best practices for AI/ML platforms.

- Ensure AI solutions align with enterprise security, compliance, and performance standards.

Generative AI & LLM Solutions :

- Architect and implement LLM-powered applications for enterprise use cases.

- Design and deploy Retrieval-Augmented Generation (RAG) systems.

- Build and optimize embedding pipelines and semantic search solutions.

- Integrate vector databases (Pinecone, Weaviate, Milvus, FAISS, Chroma).

- Evaluate, fine-tune, and optimize LLMs for accuracy, latency, and cost.

Model Development & Frameworks :

- Provide hands-on guidance using Python, TensorFlow, PyTorch, and related ML frameworks.

- Lead model selection, experimentation, fine-tuning, and evaluation.

- Support MLOps best practices including model versioning, CI/CD, and monitoring.

- Define strategies for model explainability, bias detection, and fairness.

Data Engineering & Platforms :

- Collaborate with data engineering teams to design feature stores, data lakes, and streaming pipelines.

- Ensure data quality, lineage, and governance across AI pipelines.

- Architect solutions on cloud-native AI platforms (AWS, Azure, GCP).

Governance, Security & Compliance:

- Implement AI governance frameworks covering model lifecycle, risk management, and compliance.

- Ensure secure handling of data and prompts in GenAI solutions.

- Address regulatory, ethical AI, and privacy considerations.

Technical Leadership & Collaboration :

- Act as a technical leader and mentor for AI/ML engineers and data scientists.

- Partner with product, business, and IT stakeholders to translate requirements into AI architectures.

- Lead architecture reviews, proof-of-concepts, and technology evaluations.

- Strong hands-on expertise in Python.

- Deep experience with TensorFlow, PyTorch, and ML model development.

- Proven experience in LLM and Generative AI ecosystems.

- Strong expertise designing RAG architectures, embeddings, and semantic search.

- Hands-on experience with vector databases and similarity search techniques.

- Experience building production-grade AI systems at enterprise scale.

Platforms & Tools :

- Cloud platforms: AWS, Azure, or GCP (SageMaker, Vertex AI, Azure ML).

- MLOps tools: MLflow, Kubeflow, Airflow, CI/CD pipelines.

- Containerization: Docker, Kubernetes.

- API development and integration (REST/gRPC)