Senior Machine Learning Engineer

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Open role / Full-time

Full-timeSenior

Kozhikode · Hybrid

Senior Machine Learning Engineer

Architect, build, and deploy production-grade ML pipelines, LLM fine-tuning, and RAG architectures for healthcare and visual intelligence systems.

Apply for this role 01Intermediate capability 02Real product work 03Hybrid collaboration

Role standard

Ready to contribute. Still ready to learn.

Typically 4+ years of hands-on experience designing, training, and deploying AI models in production.

What matters most

Show us how you think, what you have built or learned, and how you improve work after feedback.

Responsibilities

What you will own.

1. 01Design and implement reliable pipelines for model training, evaluation, and deployment.
2. 02Build robust retrieval-augmented generation (RAG) frameworks with clean indexing and validation.
3. 03Optimize model performance, memory footprint, and API latency for high-throughput client systems.
4. 04Collaborate with product and data engineering teams to transition ideas into scalable microservices.
5. 05Establish best practices in data versioning, experiment tracking, and model monitoring.

Intermediate skills

What you should bring.

• Strong Python proficiency and deep understanding of PyTorch or TensorFlow
• Experience with LLM orchestration frameworks (LangChain, LlamaIndex) and API integrations
• Hands-on vector search engineering (pgvector, Qdrant, Pinecone)
• Applied MLOps fundamentals: Docker, container orchestration, MLflow, and cloud deployments
• Deep understanding of statistical testing, evaluation metrics, and error analysis

Useful, not required: Experience in healthcare data standards, Edge AI, tensor optimization (TensorRT), or custom fine-tuning of open-weight models is a strong plus.

Early outcomes

What good progress looks like.

01

A scalable, production-ready pipeline for generative or computer vision workflows

02

Measurable improvement in model inference latency, retrieval accuracy, or precision metrics

03

Documented evaluation framework and drift monitoring strategy for active services

Hiring path

Clear and role-relevant.

01

Application review

Resume, portfolio and evidence of practical ability.

02

Working conversation

Your thinking, interests and relevant experience.

03

Practical review

A focused task or work sample when useful for the role.

04

Final alignment

Scope, expectations, location and next-step clarity.

The sequence may vary by role. Xynvia does not ask candidates to complete unpaid production work.

Interested / apply directly

Bring the work that shows your thinking.

Attach your resume and include a portfolio, GitHub, case study or relevant work sample where available.

Apply for Senior Machine Learning Engineer