Senior AI/ML Engineer — Hands-on AI Lead

Cyurae; Gurugram District; On-site; INR 3,500,000 - 7,000,000; Full time

Benefits offered by this job

High ownership in AI team

Direct collaboration with CTO/Founding

Hands-on ML/DL work

Competitive compensation

Job summary

CYURÆ is hiring a senior AI/ML engineer to lead model development and take AI systems from experimentation to production in Gurugram.

You will collaborate with the CTO, product and engineering teams, own architecture, fine-tuning, evaluation and deployment, and mentor the current AI engineer. This role is on-site in Gurugram with immediate impact on product direction.

Qualifications

• 5+ years of AI/ML or data science experience.
• Strong Python production skills.
• Experience fine-tuning models (LLMs, vision, multimodal).

Responsibilities

• Design and own AI architecture and pipelines.
• Mentor the AI Engineer and support future hiring.
• Evaluate models and ensure production readiness.
• Collaborate with CTO, product, and developers.

Skills

AI/ML expertise

Mentoring

Production deployment

Problem solving

Tools

PyTorch

Hugging Face

FastAPI

PostgreSQL

Redis

Job description

CYURÆ is building an AI-powered fashion intelligence platform focused on personalised styling, visual understanding, recommendation systems and premium digital fashion experiences. We need a senior, hands-on AI professional who can lead model development, guide our existing AI Engineer and take AI systems from experimentation to production.

About the Role

Company: CYURÆ

Location: Gurugram, Haryana

Work Mode: Full-time, On-site

Experience: 5+ years

Reports to: CTO / Founding Team

About CYURÆ

CYURÆ is building an AI-powered fashion intelligence platform focused on personalised styling, visual understanding, recommendation systems and premium digital fashion experiences. We need a senior, hands-on AI professional who can lead model development, guide our existing AI Engineer and take AI systems from experimentation to production.

About the Role

We are hiring a Senior AI/ML Engineer with 5+ years of practical experience in AI, machine learning and deep learning. You will own the design, fine-tuning, evaluation, deployment and continuous improvement of CYURÆ’s AI systems.

This is not a prompt-engineering-only role. You should understand the complete AI lifecycle:

Problem definition → data preparation → model selection → fine-tuning → evaluation → deployment → monitoring → improvement

You will work closely with the CTO, product team, fashion experts, backend developers, mobile developers and UI/UX designers.

Key ResponsibilitiesAI Architecture and Technical Ownership

• Design and own CYURÆ’s AI and machine-learning architecture.
• Convert product requirements into AI workflows, model pipelines, APIs and measurable evaluation criteria.
• Decide when to use hosted models, open-source models, fine-tuned models, recommendation algorithms, retrieval systems or deterministic rules.
• Build model-agnostic services so models and providers can be replaced without major application changes.
• Review AI code, experiments and model performance.
• Mentor the existing AI Engineer and support future AI hiring.

Model Fine-Tuning and Customisation

• Fine-tune language, vision, multimodal and embedding models for CYURÆ-specific use cases.
• Build and maintain training, validation, test and human-reviewed evaluation datasets.
• Work with LoRA, QLoRA, PEFT, adapter tuning, quantisation and knowledge distillation.
• Handle dataset cleaning, deduplication, annotation, augmentation, class balancing and quality checks.
• Track model versions, checkpoints, training parameters and experiment results.
• Reduce hallucination, inconsistency and bias using improved data, validation and guardrails.
• Optimise models for accuracy, latency, GPU memory, throughput and cost.

Multimodal AI and Computer Vision

• Build systems combining text, images and structured user or product data.
• Develop image classification, visual embeddings, semantic image search, garment attribute extraction, image similarity, object detection, segmentation, colour extraction and visual tagging.
• Evaluate and use CLIP, SigLIP, ViT, vision transformers and vision-language models.
• Build confidence scoring, fallback logic and human-review flows for low-confidence predictions.
• Compare third-party vision APIs with self-hosted or fine-tuned models based on quality, privacy, latency and cost.

Recommendation and Personalisation

• Design personalised recommendation, ranking and reranking systems.
• Build user, style, item and interaction representations using embeddings and structured features.
• Develop candidate generation, filtering, compatibility scoring, ranking, diversity and feedback-learning logic.
• Handle cold-start users, new products, sparse data and changing preferences.
• Track precision, recall, NDCG, acceptance rate, diversity and coverage.
• Work with fashion experts to convert qualitative judgement into structured labels and evaluation criteria.

LLM, RAG and Structured Workflows

• Build reliable LLM and multimodal workflows using commercial and open-source models.
• Develop retrieval-augmented generation using embeddings, vector search, metadata filters and hybrid retrieval.
• Implement structured outputs using JSON schemas, tool calling, validators, retries, confidence thresholds and fallback models.
• Version prompts, model configurations and evaluation datasets.
• Create automated and human-reviewed evaluation suites to prevent regressions.
• Keep model-generated outputs separate from deterministic product and business logic.

Production AI Engineering and MLOps

• Convert notebooks and proofs of concept into maintainable production services.
• Build model-serving APIs using Python and FastAPI.
• Containerise AI services with Docker and deploy them on AWS.
• Work with services such as Bedrock, SageMaker, ECS/EKS, S3, SQS, CloudWatch, PostgreSQL and Redis.
• Use Hugging Face, vLLM, TGI, Triton or equivalent inference frameworks.
• Build model and prompt versioning, rollback, monitoring and cost-tracking systems.
• Implement logging, tracing, latency monitoring, error handling, retries and asynchronous processing.
• Optimise inference through batching, caching, quantisation and model routing.
• Maintain clean code, tests, documentation and deployment runbooks.

Evaluation, Security and Privacy

• Define quality, latency, safety, reliability and cost thresholds before releasing models.
• Build test cases for standard, edge, multilingual, low-quality and adversarial inputs.
• Monitor model drift, data drift, quality degradation and inference costs.
• Protect user images, profile data and preferences during training and inference.
• Prevent prompt injection, data leakage, cross-user exposure and unauthorised retrieval.
• Maintain traceability for model version, prompt version, input source, output, latency and cost.
• Follow privacy-by-design, least-privilege and data-minimisation principles.

Required Qualifications

• 5+ years of professional experience in AI, ML, deep learning or applied data science.
• Strong production-level Python skills.
• Advanced practical experience with PyTorch and Hugging Face.
• Proven experience fine-tuning open-source language, vision, embedding or multimodal models.
• Hands-on knowledge of LoRA, QLoRA, PEFT, quantisation or similar techniques.
• Experience building repeatable training, validation and evaluation pipelines.
• Strong understanding of transformers, embeddings, attention mechanisms and deep-learning fundamentals.
• Experience in at least two areas: LLMs, computer vision, multimodal AI, recommendation systems, personalisation or semantic search.
• Experience deploying AI systems into production and performing model evaluation and error analysis.
• Experience with FastAPI or another Python API framework.
• Working knowledge of PostgreSQL, Redis and vector search tools such as pgvector, Pinecone, Qdrant, Weaviate, Milvus or OpenSearch.
• Experience with Docker, CI/CD, Git and cloud infrastructure.
• Experience with AWS or an equivalent cloud platform.
• Ability to mentor engineers, review code and make architecture decisions.
• Comfortable working full-time from our Gurugram office.

Preferred Qualifications

• Experience in fashion-tech, retail, e-commerce or consumer applications.
• Experience building recommendation, ranking or visual-search systems at scale.
• Knowledge of CLIP, SigLIP, ViT, segmentation models, diffusion models or multimodal foundation models.
• Experience with MLflow, Weights & Biases, DVC or similar tools.
• Knowledge of vLLM, TGI, Triton, ONNX or TensorRT.
• Experience with multilingual AI, particularly English, Hindi or Hinglish.
• Experience in an early-stage or zero-to-one product environment.
• Open-source contributions, research publications or patents are a plus.

This Role Is Not Suitable For

• Candidates whose experience is limited to prompt writing or third-party AI API integration.
• Candidates with only coursework, certifications or notebook-level projects and no production deployment.
• Candidates unable to explain the dataset, architecture, metrics and business outcome of their AI projects.
• Candidates seeking only a management role without hands-on development.

What Success Looks Like

During the first three to six months, you should be able to:

• Review and improve the current AI architecture.
• Establish model, prompt, dataset and evaluation versioning.
• Define measurable baselines for quality, latency, reliability and cost.
• Complete at least one relevant model fine-tuning or adaptation project.
• Deploy production-ready AI APIs with monitoring and rollback support.
• Improve recommendation or visual-understanding quality using measurable results.
• Mentor the current AI Engineer and strengthen the team’s engineering practices.

What We Offer

• High ownership in CYURÆ’s founding AI team.
• Direct collaboration with the CTO, CEO and core product team.
• Opportunity to shape model strategy, architecture and product direction.
• Hands-on work across fine-tuning, multimodal AI, computer vision, recommendations and MLOps.
• Competitive compensation based on experience and demonstrated capability.