---
title: 'Sr. Machine Learning Engineer (Remote, Contract) [HR216] (PK) at Smart Working Solutions'
canonical: 'https://feeny.ai/job/sr-machine-learning-engineer-remote-contract-hr216-pk-smart-working-solutions-mvzknam5mpjn'
type: 'job'
last_seen: '2026-10-15'
---

# Sr. Machine Learning Engineer (Remote, Contract) [HR216] (PK) at Smart Working Solutions

- **Company:** Smart Working Solutions
- **Location:** Pakistan
- **Employment:** part-time
- **Work type:** remote
- **Posted:** 2026-10-07
- **Last confirmed live:** 2026-10-15
- **Apply:** https://jobs.lever.co/smart-working-solutions/935fa25e-fd79-42c5-8e55-805e455c5082

## 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 in Applied AI, Machine Learning and MLOps to build and modernise an AI platform. The role combines Applied AI, MLOps and backend/platform engineering, with a strong focus on productionising, deploying, evaluating and operating ML/AI systems. You will build new ML capabilities, modernise existing NLP and generative AI systems, and create reliable, observable infrastructure that makes 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.

## 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 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 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.

## About Smart Working Solutions

## Company Overview
- **One-liner**: Smart Working provides a "Tech Talent as a Service" model, delivering rigorously vetted remote software engineers and AI specialists who are fully embedded into clients’ teams.
- **Entity Type**: Private (Privately Held)
- **Headquarters**: London, United Kingdom
- **Founded**: Not publicly available
- **Founders**: Oli Rowse (Co-Founder), Max Rowse (Co-Founder), Julian Rowse (Director & Founder)

## Core Business
- **Primary industry**: IT Services and IT Consulting
- **Target customers**: B2B – mid‑market to enterprise clients in the UK and USA seeking to scale engineering teams without traditional hiring overhead
- **Mission / purpose**: "Building high‑performance global engineering teams without the friction, risk, or overhead of legacy hiring models."

## Products & Services
- **Tech Talent as a Service (TTaaS)**: A fully managed service that includes vetting, onboarding, payroll, HR administration, performance management, compliance, and continuous AI training. Engineers work the client’s hours, join their standups, and integrate into existing Slack/communication channels. Pricing starts at ~£20–£25/hour all‑inclusive.

## Market Standing
- **Valuation / Market Cap**: Not disclosed
- **Key Metric**: 123 employees (78% year‑over‑year growth); serves 60+ clients in the UK and USA
- **Notable Investors / Partners**: Not publicly listed
- **Growth Signals**:
  - 78% headcount growth YoY
  - Average engineer tenure of 26 months (low churn for the industry)
  - Net Promoter Score (NPS) of 75
  - ISO 27001 certified
  - 4‑stage vetting process that admits only ~1% of ~100,000 monthly applicants
  - Continuous AI Academy training for all engineers

## Competitive Advantages
- **Rigorous vetting**: 4‑stage process (AI‑assisted screening, advanced technical assessments, communication tests, live technical interview) ensures top‑1% talent.
- **Full embedding**: Engineers work the client’s exact hours, attend meetings, and use the client’s tools – eliminating the disconnect typical of offshore outsourcing.
- **Low churn guarantee**: Average 26‑month tenure; if an engineer leaves, they are replaced at no extra cost.
- **All‑inclusive pricing**: Single monthly invoice covers vetting, onboarding, payroll, HR, compliance, performance management, and AI training.
- **Security & compliance**: ISO 27001 certified with comprehensive background screening.

## Strategic Focus
- Scaling the “Tech Talent as a Service” model to more clients while maintaining high quality and low churn.
- Continuous investment in AI training (AI Academy) to keep engineers ahead of emerging technologies.
- Expanding the client base beyond the UK and USA into other English‑speaking markets.

## Why Work Here
- **Culture & ratings**: Employer rating of 4.7/5 on LinkedIn (196 reviews) with strong scores for work‑life balance (4.7), compensation (4.5), culture (4.6), and career growth (4.5).
- **Remote‑first**: All roles are fully remote, with engineers distributed across 18 countries. The model supports full‑time, contractual, and part‑time engagements.
- **Stability & impact**: Engineers work directly with 60+ clients on meaningful products and receive continuous training in AI and modern tech stacks.
- **Support**: Dedicated Account Managers and Customer Success teams handle performance, compliance, and career development.
- **Perks**: All‑inclusive payroll handling, no lock‑in contracts (1‑month calibration + 6‑month minimum), and a replacement guarantee.

## Sources
1. [smartworking.io](https://smartworking.io/)
2. [linkedin.com](https://uk.linkedin.com/company/smartworkingsolutions)
3. [lever.co](https://jobs.lever.co/smart-working-solutions)
4. [simplify.jobs](https://simplify.jobs/c/Smart-Working-Solutions)

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