Driving end-to-end GenAI architecture and implementation:

• Design and deploy multi-agent systems using modern frameworks (LangGraph, CrewAI, AutoGen)
• Architect RAG solutions with advanced vector store integration
• Implement efficient fine-tuning strategies for foundation models
• Develop synthetic data generation pipelines for training and testing

Leading ML infrastructure and deployment:

• Design high-performance model serving architectures
• Implement distributed training and inference systems
• Establish MLOps practices and pipelines
• Optimize cloud resource utilization and costs
• Set up monitoring and observability solutions

Driving technical excellence and innovation:

• Define architectural standards and best practices
• Lead technical decision-making for AI/ML initiatives
• Ensure scalability and reliability of AI systems
• Implement AI governance and security measures
• Guide teams on advanced AI concepts and implementations

Overseeing production AI systems:

• Manage model deployment and versioning
• Implement A/B testing frameworks
• Monitor system performance and model drift
• Optimize inference latency and throughput
• Ensure high availability and fault tolerance

Fostering collaboration and growth:

• Mentor engineering teams on AI architecture
• Collaborate with stakeholders on technical strategy
• Drive innovation in AI/ML solutions
• Share knowledge through documentation and training
• Lead technical reviews and architecture discussions

8+ years experience in software engineering or architecture, including:

• 4+ years leading cross-functional GenAI/ML teams
• Production experience with distributed AI systems
• Enterprise-scale AI architecture implementation

To lead and architect enterprise-scale GenAI/ML solutions, focusing on:

• Multi-agent orchestration using LangGraph, CrewAI, and AutoGen
• Workflow automation with LlamaIndex, LangChain, and LangFlow
• Agent coordination using LETTA framework
• Integration of specialized agents for reasoning, planning, and execution

To design and implement sophisticated AI architectures incorporating:

• Advanced RAG systems using:
  • Vector databases (Chroma, Weaviate, Pinecone, Milvus)
  • Hybrid search with BM25 and semantic embeddings
  • Self-querying and recursive retrieval patterns
• Fine-tuning strategies for foundation models:
  • PEFT methods (LoRA, QLoRA, Adapter-tuning)
  • Parameter-efficient training approaches
  • Instruction fine-tuning and RLHF
• Multi-agent frameworks integrating:
  • Tool-use and reasoning chains
  • Memory systems (short-term and long-term)
  • Meta-prompting and reflection mechanisms
  • Agent communication protocols

Expertise in advance data generation and synthesis:

• Synthetic data generation using Arigilla and PersonaHub
• Privacy-preserving data synthesis
• Domain-specific data augmentation
• Quality assessment of synthetic data
• Data balancing and bias mitigation

To architect high-performance ML serving infrastructure focusing on:

• Model serving platforms (BentoML, Ray Serve, Triton)
• Real-time processing with Ray, Kafka, and Spark Streaming
• Distributed training using Horovod, DeepSpeed, and FSDP
• vLLM and TGI for efficient inference
• Integration patterns for hybrid cloud-edge deployments

To drive cloud architecture decisions across:

• Kubernetes orchestration with Kubeflow and KServe
• Serverless ML with AWS Lambda, Azure Functions, Cloud Run
• Auto-scaling using HPA, KEDA, and custom metrics
• Resource optimization with Nvidia Triton and TensorRT
• MLOps platforms (MLflow, Weights & Biases, DVC)

• Research publications in AI/ML
• Open-source project maintenance
• Technical blog posts on AI architecture
• Conference presentations
• AI community leadership

• Best in class salary: We hire only the best, and we pay accordingly.
• Proximity Talks: Meet other designers, engineers, and product geeks — and learn from experts in the field.
• Keep on learning with a world-class team: Work with the best in the field, challenge yourself constantly, and learn something new every day.