Senior ML Engineer (Remote, Contract) [HR216]

Smart Working; Pune District; Remote; INR 1,800,000 - 3,000,000; Full time

Job summary

Smart Working, a remote-first company, seeks a Senior ML Engineer to build and modernise an AI platform spanning Applied AI, ML and MLOps. The role focuses on productionising, deploying and operating AI/ML apps with strong backend engineering support.

You will evolve NLP, generative AI and other ML capabilities, architecting scalable, observable systems and robust pipelines for both batch and real-time workloads.

Qualifications

• 6+ years of professional AI/ML experience with production exposure.
• 5+ years of professional MLOps experience.
• 2+ years of real Applied AI experience beyond experiments.
• Strong Python experience as core language.
• Experience with Docker and Kubernetes.
• Proven productionising and deploying AI/ML apps.

Responsibilities

• Refactor, modernise and productionise ML models and AI capabilities.
• Build new ML components and modular production-ready services.
• Develop production ML applications using Python.
• Maintain ML pipelines for deployment and operation.
• Engineer resilient ML workflows with retry logic and error handling.
• Design automated model evaluation pipelines with golden datasets.
• Evaluate models using appropriate metrics for diverse AI use cases.
• Implement guardrails for grounding and hallucination in generative AI.
• Apply RAG and other Applied AI techniques where appropriate.
• Design provenance for model and data to support auditability.
• Build infrastructure for shadow testing, A/B testing and fail‑safe deployment.
• Support labeling and curation of golden datasets.
• Create feedback loops for improving ML datasets.
• Integrate external AI APIs and adapters.
• Implement observability and telemetry for model behavior and cost.

Skills

Python

Docker

Kubernetes

AI/ML

MLOps

NLP

Generative AI

RAG

Model evaluation

Productionising

Backend APIs

Observability

Tools

Python

Docker

Kubernetes

Job description

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.

Responsibilities

• 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.

• At least 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.

• Experience with Docker and Kubernetes.

• Proven experienceproductionising 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.

Nice to Have

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

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

• 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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgement. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.