Senior ML Engineer (Remote, Contract) [HR216]

Smart Working; Bengaluru; Remote; INR 900,000 - 1,500,000; Full time

Job summary

Smart Working is hiring a Senior ML Engineer to build and modernise an AI platform, with strong focus on Applied AI, NLP and MLOps. You will productionise, deploy and operate AI/ML applications, while improving observability and reliability of ML systems.

The role sits at the intersection of Applied AI and MLOps, requiring backend engineering support to productionise ML capabilities and integrate them into the broader platform. A remote-first environment is offered.

Qualifications

• 6+ years of professional AI/ML experience, with production exposure.
• 5+ years of professional MLOps experience.
• At least 2+ years of real Applied AI experience beyond experiments.
• Strong Python proficiency as core language.
• Proven productionising and deploying AI/ML applications and models.
• Backend engineering experience to integrate ML components into services.

Responsibilities

• Refactor, modernise and productionise ML models and AI capabilities.
• Build new ML components and reusable production-ready modules.
• Develop production ML applications and supporting services using Python.
• Create and maintain ML pipelines for evaluation, deployment and operation.
• Design guardrails for grounding, hallucination and quality of AI outputs.
• Implement observability and telemetry for model behavior and costs.
• Support integration of ML components into the wider application.

Skills

Python

ML Ops

Applied AI

MLOps

NLP

RAG

Generative AI

Back-end API

Tools

Docker

Kubernetes

FastAPI

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.

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.

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.
• 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.

Nice to Have

• 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.

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 judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.