Senior AI Engineer / AI Technical Lead

Generative AI | Multimodal AI | Recommendation Systems

Company: CYURÆ

Location: Gurugram, Haryana

Work Mode: Full-time, On-site

Experience: Minimum 3+ years; 5+ years preferred

Reports To: CTO / Founding Team

About CYURÆ

CYURÆ is building an AI-powered fashion intelligence platform focused on personalised experiences, visual understanding, intelligent recommendations and generative AI.

Our engineering team includes mobile, backend, frontend and junior AI resources. We are looking for a senior, hands-on AI professional who can lead AI planning and execution, mentor our existing AI resource and turn ideas into reliable product capabilities.

About the Role

You will own the architecture, development, evaluation and delivery of CYURÆ’s AI systems, working closely with the founding team, product team and engineers.

This is a hands-on technical leadership role, not a prompt-writing-only or management-only position. You should have delivered an AI product or significant AI feature into production and be able to explain your contribution, technical decisions and results.

Production product delivery is essential. Additional expertise in cloud deployment, DevOps and infrastructure management is an advantage.

Key Responsibilities1. AI Product Planning and Technical Leadership

- Translate product requirements into practical AI architecture, experiments, milestones and sprint-ready tasks.
- Assess feasibility, data requirements, model quality, delivery timelines and infrastructure costs.
- Lead model-selection decisions, technical reviews and coordination with backend, frontend and React Native developers.
- Mentor junior AI resources, review their work and take ownership of delivery from prototype to production.

1. Generative AI and Text-to-Image Systems

- Develop text-to-image and image-generation workflows using suitable hosted or open-source models.
- Evaluate diffusion models and generative AI frameworks for quality, speed, consistency, control and cost.
- Customise or fine-tune models using appropriate techniques, including LoRA and other parameter-efficient approaches.
- Build preprocessing, generation, validation and post-processing pipelines with quality checks and fallback mechanisms.

1. Recommendation and Personalisation

- Develop recommendation, retrieval, ranking, reranking and personalisation systems.
- Build text, image, user and product representations using embeddings and structured features.
- Implement candidate generation, filtering, similarity search and compatibility scoring.
- Address cold-start users, sparse data and changing preferences, and improve recommendations through evaluation and feedback.

1. Multimodal AI and Model Integration

- Build workflows combining text, images and structured information, including image-to-text capabilities where required.
- Evaluate and integrate OpenAI APIs, Hugging Face models and other commercial or open-source AI models.
- Work with transformers, vision-language models, image encoders and embedding models.
- Create modular model integrations with structured outputs,



validation, retries and fallbacks so providers can be changed without major redevelopment.

1. AI Pipelines and Event-Driven Architecture

- Design synchronous, asynchronous and batch-processing AI pipelines.
- Build event-driven workflows using queues, background workers and schedulers.
- Handle long-running jobs, retries, failure recovery and dead-letter processing.
- Define clear API contracts with the Node.js and backend teams, covering data ingestion, inference, output processing and feedback.

1. Production Engineering and MLOps

- Convert experiments and notebooks into maintainable AI services using Python, FastAPI or equivalent frameworks.
- Work with engineering teams on containerisation, cloud deployment and CI/CD.
- Maintain model, prompt, dataset and experiment versioning, with documentation and rollback procedures.
- Improve inference performance through appropriate batching, caching, quantisation and model routing.
- Contribute to deployment and infrastructure decisions involving Docker, Kubernetes and infrastructure-as-code tools.

1. Evaluation, Security and Reliability

- Build evaluation datasets, benchmarks and regression tests for AI capabilities.
- Define and track quality, relevance, consistency, latency, reliability and cost metrics.
- Investigate model failures, monitor quality changes and establish automated checks and human-review processes.
- Protect user data and implement appropriate access controls, privacy safeguards and AI security measures.
- Ensure releases include testing, logging, monitoring and failure-recovery mechanisms.

Required Qualifications
- Minimum 3+ years of professional experience in AI, machine learning, deep learning or applied AI development.
- Demonstrated contribution to at least one AI product or major AI feature used in production, beyond an academic project or prototype.
- Strong Python programming skills and practical experience with PyTorch, Hugging Face Transformers and related AI frameworks.
- Hands-on experience with text-to-image or multimodal AI workflows, alongside recommendation, personalisation, retrieval or ranking systems.
- Experience integrating multiple commercial or open-source AI models, including OpenAI APIs and models available through Hugging Face.
- Understanding of transformers, embeddings, vector search, model evaluation and deep-learning fundamentals.
- Experience building AI APIs and pipelines, including asynchronous processing, queues and reliable error handling.
- Working knowledge of Docker, Git and CI/CD practices.
- Ability to plan AI development, make architecture decisions, communicate trade-offs and mentor junior engineers.




- Willingness to work full-time from our Gurugram office or relocate to Gurugram.

Preferred Qualifications
- Five or more years of AI/ML experience or previous hands-on technical leadership in a product team.
- Model fine-tuning experience using LoRA, QLoRA, PEFT or comparable approaches, supported by clear evaluation results.
- Experience with diffusion models, Hugging Face Diffusers, vision-language models, CLIP, SigLIP or similar technologies.
- Experience with vector search platforms such as pgvector, Qdrant, Pinecone, Weaviate, Milvus or OpenSearch.
- Knowledge of Kubernetes and infrastructure-as-code tools such as Terraform, Pulumi or CloudFormation.
- Hands-on deployment experience on AWS, familiarity with GPU workloads and experience improving inference cost or latency.
- Experience with experiment tracking, model serving and monitoring tools such as MLflow, Weights & Biases, vLLM or Triton.
- Experience in fashion-tech, e-commerce, retail, consumer applications or an early-stage product setting.
- Candidates based in Gurugram or Delhi NCR are preferred; candidates willing to relocate are welcome.

What We Are Looking For A hands-on engineer who can lead AI delivery, not just complete assigned experiments. You should be comfortable selecting appropriate models, building complete pipelines, evaluating results and working with other engineers to deliver reliable application features. You should balance quality, delivery timelines and cost, explain limitations honestly and remain accountable for the performance of AI systems after release. Experience limited to prompt writing, basic API wrappers or notebook demonstrations will not meet the requirements of this role.

What Success Looks Like

During the first three to six months, you will be expected to:
- Prepare a practical AI execution roadmap with clear priorities and delivery milestones.
- Establish model evaluation, versioning and engineering standards.
- Deliver a production-ready generative or multimodal pipeline and advance recommendation capabilities.
- Integrate AI services with the backend and mobile application, with monitoring for quality, latency, failures and cost.
- Mentor the existing AI resource and improve the team’s delivery capability.

What We Offer A high-ownership role within CYURÆ’s core product team, direct collaboration with the founders, and the opportunity to influence AI architecture, model selection and future team development. Compensation will be based on experience, technical depth and demonstrated product delivery.

How to Apply

Send your updated CV, LinkedIn profile and relevant project portfolio or GitHub/GitLab profile to [email protected] .

Include your current location,

total AI/ML experience,

current CTC,

expected CTC,

notice period and willingness to work in office in Gurugram.

Please briefly describe one AI product or major feature you helped deliver into production, your exact contribution, the models and pipelines used, and how you evaluated its performance.

Highlight relevant text-to-image, multimodal AI and recommendation-system experience

📌 Senior AI Engineer / AI Technical Lead (Gurugram)
🏢 Cyurae
📍 Gurugram