Associate ML Infrastructure Engineer

Role overview

Responsibilities

• check_circle AI Solution Architecture & Design Lead the endtoend design of AI and Generative AI solutions , including system architecture, data flows, model selection, and integration patterns.
• check_circle Partner with customers, product managers, and business teams to identify highimpact AI-driven use cases .
• check_circle Evaluate AI technologies, frameworks, and vendors to recommend optimal solution approaches.
• check_circle HandsOn Development & PoC/MVP Creation Build proofs of concept (PoC), MVPs, and production-grade features using Vibe coding, LLMs, embeddings, retrieval, agents, and advanced AI pipelines.
• check_circle Implement AI workflows such as MCP, RAG, finetuning, prompt engineering, agentic orchestration , and model optimization.
• check_circle Develop integrations with enterprise systems, APIs, data platforms, and cloud environments (AWS/Azure/Google Cloud Platform).
• check_circle Technical Expertise & Evangelism Serve as a subject-matter expert and advisor on modern AI, GenAI, and LLM-based architectures .
• check_circle Stay up to date with advancements in AI infrastructure, LLM capabilities, vector databases, and agentic automation.
• check_circle Technical Skills Strong handson experience with LLMs, GAI frameworks, and modern AI stacks (OpenAI, Azure OpenAI, Anthropic, Meta Llama, etc.).
• check_circle Experience building AI pipelines using: MCP/RAG architectures Agents / multiagent orchestration Prompt engineering & evaluation techniques Finetuning or parameter-efficient training (LoRA, adapters, etc.)
• check_circle Proficiency in Vibe coding using the latest AI Code generation tools
• check_circle Proficiency in Python and/or Node.js, including: LangChain / Semantic Kernel / LlamaIndex ML frameworks (PyTorch, TensorFlow optional)
• check_circle Experience with vector databases (Pinecone, Weaviate, Chroma, Redis Vector).
• check_circle Knowledge of cloud AI services: Azure, AWS, Google Cloud Platform
• check_circle Strong understanding of API architectures, microservices, DevOps, CI/CD, and containerization.
• check_circle Proven ability to translate business needs into scalable AI designs.
• check_circle Experience interacting with customers, gathering requirements, and presenting technical solutions.
• check_circle Strong documentation, architecture diagramming, and communication skills.
• check_circle 5 10+ years in software engineering or solution engineering.
• check_circle 2 3+ years specifically in AI/ML or GenAI implementation.
• check_circle Bachelor s/Master s in Computer Science, Engineering, AI/ML, or related fields.

Preferred qualifications

• Experience with telco and media, or large enterprise environments.
• Knowledge of data engineering, ETL pipelines, feature stores.
• Experience with MLOps or LLMOps methodologies and tools.
• Exposure to vector search optimization, knowledge graphs, or reinforcement learning.
• Familiarity with responsible AI, security, governance, and compliance frameworks.