Senior Solutions Architect - AI/ML (8-11 yrs)

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.