Senior Solutions Architect (AI/ML) - Contractual

We are looking for a Senior Solutions Architect to design, develop, and scale innovative AI/ML-driven solutions. You will be responsible for architecting highly scalable, low-latency distributed systems optimized for AI/ML workloads. As a key technical leader, you will solve complex challenges, influence next-generation AI/ML infrastructures, and guide cross-functional teams to deliver state-of-the-art solutions for fast-growing startups and enterprise companies.

Be at the forefront of shaping next-generation AI/ML infrastructures, driving solutions for high-impact products across diverse industries. You'll have the opportunity to influence key architectural decisions and enable real-world applications that scale globally, ensuring innovation and efficiency at every step.

Requirements

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

Drivng 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 exoerience 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

Benefits

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.

About us —

We are Proximity — a global team of coders, designers, product managers, geeks, and experts. We solve complex problems and build cutting-edge tech at scale. Here's a quick guide to getting to know us better:

• Watch our CEO, Hardik Jagda, tell you all about Proximity.
• Read about Proximity's values and meet some of our Proxonauts here.
• Explore our website, blog, and the design wing — Studio Proximity.
• Get behind the scenes with us on Instagram! Follow @ProxWrks and @H.Jagda

Originally posted on Himalayas