Senior Solutions Architect (AI/ML) - Contractual

you ll be responsible for
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

you need
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)

bonus points for

• Research publications in AI/ML

• Open-source project maintenance

• Technical blog posts on AI architecture

• Conference presentations

• AI community leadership

what you get

• 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.