AI Ml engineer

Position: AI/ML Engineer

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Opportunity

Were looking for an AI/ML Engineer to design build and operate intelligent systems in production. Youll architect LLM-powered pipelines deploy and scale ML models and integrate AI capabilities into core product features owning the full journey from model selection to live inference.

High Level Responsibilities

Provide direction and clarity for the AI/ML endeavors of the team aligned with the latest changes in the tech world to keep the product/company competitive in domain of AI/ML

Architect and ship production-grade AI/ML systems including LLM pipelines NLP models and agentic workflows

Build and maintain robust serving infrastructure: APIs inference endpoints and real-time or batch processing pipelines

Integrate LLMs via RAG function calling and tool use; implement guardrails evaluation and hallucination mitigation

Own MLOps end-to-end model versioning CI/CD for ML monitoring drift detection and rollback strategies

Collaborate with Product UI/UX and Platform teams to embed AI features into customer-facing applications

Optimize models and infrastructure for latency throughput cost and reliability at scale

Contribute to architecture decisions and establish engineering best practices for the AI stack

Contribute in all phases of the Product Development Lifecycle

Work closely with UI/UX Architecture DevOps and Testing teams while continuing to focus on the customer and business value

Optimize algorithms for maximum speed performance and scalability

Preferred Skills and Qualifications

4 years of experience building and deploying ML/AI systems in production environments

Strong Python engineering skills clean testable production-quality code; Java or Go a plus

Hands-on experience with LLM frameworks: LangChain LlamaIndex or equivalent; deep familiarity with GPT-4 Gemini or LLaMA model families

Proficiency with ML libraries: HuggingFace Transformers PyTorch or TensorFlow

Experience building and operating RAG systems vector databases (Pinecone Weaviate pgvector) embedding pipelines and retrieval tuning

Solid MLOps background: MLflow BentoML or similar; containerization (Docker/Kubernetes); cloud deployment on AWS or Azure

Comfort with data pipelines and SQL able to own data flow from source to model input

Strong understanding of software engineering fundamentals: APIs system design version control (Git) and CI/CD

Clear communicator able to align on requirements with Product and explain system behaviour to non-technical stakeholders

B.S. or M.S. in Computer Science Software Engineering Machine Learning or equivalent practical experience

Nice to Have - Skills and Qualifications

Experience with agentic AI systems multi-step reasoning tool orchestration memory and planning

Fine-tuning or RLHF experience with open-source models (LLaMA Mistral or similar)

Familiarity with NLP tasks: NER classification information extraction from unstructured text

Knowledge of responsible AI practices bias evaluation safety testing and model governance

Computer vision or multi-modal model experience

Contributions to open-source ML/AI projects

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Required Skills:

AI/MLLLM pipelinesNLP modelsAPIsinference endpointsRAGLLMsCI/CD for MLUI/UXGPT-4Geminior LLaMA model familiesPythonLangChainLlamaIndexHuggingFace TransformersPyTorchor TensorFlow