AI/ML Engineer

Job description

Key Responsibilities

AI & System Architecture

● Design and architect enterprise-grade GenAI and Agentic AI systems

● Build scalable LLM-powered applications, RAG pipelines, multi-agent systems, and

autonomous workflows

● Drive secure, reliable, and high-performance AI solution design

Agentic AI & Advanced GenAI

● Architect and implement agent orchestration frameworks

● Design multi-agent collaboration systems and autonomous task execution pipelines

● Implement advanced prompt engineering, memory management, and reasoning chains

● Develop evaluation frameworks for LLM quality, hallucination mitigation, and model

alignment

Technical Leadership

● Lead end-to-end AI solution development from concept to production

● Mentor engineers and establish strong AI engineering practices

● Drive code quality, architectural reviews, and DevOps/MLOps best practices

● Own technical roadmap and innovation strategy for AI initiatives

AI Infrastructure & MLOps

● Build scalable model deployment pipelines (CI/CD for AI)

● Optimize inference performance, latency, and cost

● Implement monitoring, observability, and governance for AI systems

● Ensure responsible AI practices, security, compliance, and data privacy

Stakeholder Collaboration

● Translate business problems into AI-driven technical solutions

● Partner with product, engineering, and leadership teams

● Provide technical thought leadership internally and externally

Required Technical Expertise

Core AI & GenAI

● Strong hands-on experience with:

○ LLMs (OpenAI, Anthropic, open-source models like LLaMA, Mistral)

○ RAG architectures

○ LangChain / LlamaIndex

○ Vector databases (Pinecone, Weaviate, FAISS, Milvus)

● Experience building production-grade AI agents

● Advanced prompt engineering & fine-tuning techniques

● Experience with embeddings, evaluation frameworks, and guardrails b

Engineering & Architecture

● Strong expertise in Python and AI/ML ecosystem (PyTorch, TensorFlow, Hugging Face)

● Experience designing distributed, scalable microservices architectures

● Strong API design and backend system development skills

● Experience with data pipelines and real-time processing systems

Cloud & Infrastructure

● Hands-on experience with Azure / AWS / GCP

● Experience with containerization (Docker), orchestration (Kubernetes)

● Experience deploying AI workloads on cloud-native environments

● Understanding of GPU infrastructure and scaling strategies

MLOps & DevOps

● CI/CD for AI systems

● Model monitoring, drift detection, observability tools

Leadership & Soft Skills

● Proven experience leading AI engineering teams

● Strong solution architecture and system design capability

● Excellent problem-solving and critical thinking skills

● Strong communication and stakeholder management abilities

● Ability to balance innovation with production stability

Preferred Qualifications

● Experience building enterprise AI platforms

● Experience in AI governance and Responsible AI frameworks

● Exposure to multi-modal AI systems (vision + text)

● Experience with AI security and model risk mitigation