Senior ML Engineer (Remote, Contract) [HR216]

Smart Working Solutions; India; Remote; INR 4,000,000 - 6,000,000; Full time

Job summary

Smart Working is seeking a Senior ML Engineer to help build and modernise a new AI platform. The role sits between Applied AI and MLOps, with backend engineering support to productionise ML capabilities.

You will work on both building new ML components and re-engineering existing models into production-ready, observable systems. You will modernise NLP and generative AI capabilities while creating architecture that makes new models easier to integrate, evaluate, monitor and deploy.

Qualifications

• 6+ years of professional AI/ML experience with genuine production experience.
• 5+ years of professional MLOps experience.
• 2+ years of real Applied AI experience beyond experiments or personal projects.
• Strong professional Python experience; Python is the core language for this role.
• Proven productionising and deploying AI/ML applications and models.
• Strong understanding of Applied AI/ML and MLOps, not just model research.

Responsibilities

• Refactor, modernise and productionise existing ML models and Applied AI capabilities, including NLP and generative AI.
• Build new ML components and re-engineer existing models into production-ready modules.
• Develop production ML applications and supporting services primarily using Python.
• Build and maintain reliable ML pipelines covering model integration, evaluation, deployment and operation.
• Engineer resilient ML workflows with retry logic, error handling and repeatable execution.
• Design and automate model evaluation pipelines using golden datasets and quality thresholds.
• Evaluate models using metrics appropriate to outputs (generative AI, classification).
• Implement guardrails to assess grounding and hallucinations of generative AI outputs.
• Apply RAG techniques where appropriate to ML capabilities.
• Design mechanisms for model, prompt and input-data provenance for auditability.
• Build infrastructure for shadow testing, A/B testing, fallback strategies and kill switches for safe deployment.
• Support labeling and curating golden datasets for evaluation.
• Build human-in-the-loop feedback pipelines to improve datasets.
• Integrate third-party AI APIs and adapters/interfaces.
• Implement observability and telemetry covering model behavior, errors, costs and latency.
• Contribute backend engineering to integrate ML components into the wider application.
• Support batch and real-time ML workloads as the platform evolves.
• Improve platform extensibility, resilience and error tracking.

Skills

Python

ML engineering

MLOps

Applied AI

Productionising

Observability/Telemetry

RAG

Backend integration

Tools

Docker

Kubernetes

FastAPI

Job description

About Smart Working

At Smart Working, we believe your job should not only look right on paper but also feel right every day. This isn't just another remote opportunity - it's about finding where you truly belong, no matter where you are. From day one, you're welcomed into a genuine community that values your growth and well-being.

Our mission is simple: to break down geographic barriers and connect skilled professionals with outstanding global teams and products for full-time, long-term roles. We help you discover meaningful work with teams that invest in your success, where you’re empowered to grow personally and professionally.

Join one of the highest-rated workplaces on Glassdoor and experience what it means to thrive in a truly remote-first world.

About the Role

We are seeking a Senior ML Engineer with strong experience across Applied AI, Machine Learning and MLOps to help build and modernise a new AI platform.

This role sits primarily between Applied AI and MLOps, with backend/platform engineering experience supporting the productionisation of ML capabilities. You will work on both building new ML components from scratch and re-engineering existing capabilities into reliable, observable and production-ready systems. The focus is not purely on model development or experimentation. You should have genuine experience productionising, deploying, evaluating and operating AI/ML applications, with a strong understanding of the challenges encountered when running these systems in production.

You will work on modernising existing NLP, generative AI and other ML capabilities while creating an architecture that makes new models easier to integrate, evaluate, monitor and deploy.

• Refactor, modernise and productionise existing ML models and Applied AI capabilities, including NLP and generative AI solutions.

• Build new ML components and re-engineer existing models into standardised, production-ready modular components.

• Develop production ML applications and supporting services primarily using Python.

• Build and maintain reliable ML pipelines covering model integration, evaluation, deployment and operation.

• Engineer resilient ML workflows with appropriate retry logic, error handling and repeatable execution.

• Design and automate model evaluation pipelines using golden datasets and appropriate quality and performance thresholds.

• Evaluate different types of models using metrics appropriate to their outputs, including generative AI, classification and other ML use cases.

• Implement appropriate guardrails and evaluation mechanisms to assess grounding, hallucinations and quality of generative AI outputs.

• Apply Applied AI techniques, including RAG, where appropriate to the ML capabilities being developed.

• Design mechanisms for model, prompt and input-data provenance to support auditability and reproducibility.

• Build infrastructure supporting shadow testing, A/B testing, fallback strategies and kill switches for safe ML deployment.

• Support the labelling, curation and ongoing development of golden datasets used for model evaluation.

• Build structured human-in-the-loop feedback pipelines to capture reviews and corrections and improve ML datasets.

• Integrate third-party AI APIs and build appropriate adapter/API interfaces.

• Implement observability and telemetry covering model behaviour, errors, compute costs, token usage and latency.

• Contribute backend engineering capability required to integrate ML components reliably into the wider application.

• Support both batch and real-time ML workloads as the platform develops.

• Help improve the platform's extensibility, resilience, error tracking and ability to integrate new ML models efficiently.

Requirements

• 6+ years of professional AI/Machine Learning experience, with genuine production experience.

• 5+ years of professional MLOps experience.

• 2+ years of real Applied AI experience, working with AI/ML capabilities beyond experimentation or personal projects.

• Strong professional Python experience; Python is the core programming language for this role.

• Proven experience productionising and deploying AI/ML applications and models.

• Strong understanding of both Applied AI/ML and MLOps, rather than experience limited solely to model research or experimentation.

• Strong hands-on experience with model evaluation and defining appropriate quality/performance criteria for production ML systems.

• Experience working with generative AI/LLMs and understanding evaluation considerations such as grounding and hallucination.

• Hands-on understanding of RAG and other Applied AI techniques.

• Experience building and operating ML pipelines and production ML architectures.

• Experience designing reliable ML workflows with appropriate error handling, retry mechanisms and repeatable execution.

• Experience working with golden datasets and using them for model evaluation and quality gating.

• Experience building observable ML systems using appropriate logging, monitoring and telemetry.

• Understanding of model/data provenance, auditability and reproducibility.

• Experience implementing safe production deployment practices for ML systems, including appropriate testing, fallback or fail‑safe mechanisms.

• Sufficient backend engineering experience to build APIs, integrations and production‑ready services around ML capabilities.

• Experience solving real production ML problems, including reliability, deployment, integration, evaluation or performance challenges.

• Familiarity with governance, compliance and safeguards relating to sensitive data and AI-generated outputs.

Bonus/Preferred

• Experience with FastAPI for building Python-based ML APIs.

• Exposure to Argo Workflows or similar DAG‑based orchestration frameworks.

• Experience with Docker and Kubernetes.

• Experience working with one or more major cloud platforms: AWS, Azure or GCP.

• Multi-cloud or cloud‑agnostic application experience.

• Experience or understanding of TypeScript and/or Go.

• Production experience with speech-to-text or transcription models.

• Experience working with real‑time ML applications.

• Experience with traditional NLP models, transformer‑based models, encoders and decoders.

• Experience integrating external models/providers such as OpenAI or Claude.