Senior Solutions Architect
1 week ago
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.
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