{"id":2144745,"url":"https://alion.io/job/smartworkingsolutions-sr-machine-learning-engineer-remote-contract-hr216-pk","title":"Sr. Machine Learning Engineer (Remote, Contract) [HR216] (PK)","company":{"id":3745,"name":"Smart Working Solutions","domain":"smartworkingsolutions.com","url":"https://alion.io/company/smartworkingsolutions","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"contractor","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Lahore, Pakistan","Karachi, Pakistan","Islamabad, Pakistan","Peshawar, Pakistan","Rawalpindi, Pakistan","Multan, Pakistan","Pakistan"],"countries":["PK"],"hiring_countries":["PK"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":40000,"max_usd":101000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1986},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Argo Workflows","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Claude","optional":true},{"name":"Docker","optional":true},{"name":"FastAPI","optional":true},{"name":"GCP","optional":true},{"name":"Go","optional":true},{"name":"Kubernetes","optional":true},{"name":"OpenAI","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-10-07T07:08:17Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-13T13:25:34Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"About Smart Working\nAt 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.\nOur 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.\nJoin one of the highest-rated workplaces on Glassdoor and experience what it means to thrive in a truly remote-first world.\nAbout the Role\nWe are seeking a Senior ML Engineer with strong experience in Applied AI, Machine Learning and MLOps to build and modernise an AI platform.\nThe 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.\nResponsibilities\nRefactor, modernise and productionise existing ML models and Applied AI capabilities, including NLP and generative AI solutions.\n\nBuild new ML components and re-engineer existing models into standardised, production-ready modular components.\n\nDevelop production ML applications and supporting services primarily using Python.\n\nBuild and maintain reliable ML pipelines covering model integration, evaluation, deployment and operation.\n\nEngineer resilient ML workflows with appropriate retry logic, error handling and repeatable execution.\n\nDesign and automate model evaluation pipelines using golden datasets and appropriate quality and performance thresholds.\n\nEvaluate different types of models using metrics appropriate to their outputs, including generative AI, classification and other ML use cases.\n\nImplement appropriate guardrails and evaluation mechanisms to assess grounding, hallucinations and quality of generative AI outputs.\n\nApply Applied AI techniques, including RAG, where appropriate to the ML capabilities being developed.\n\nDesign mechanisms for model, prompt and input-data provenance to support auditability and reproducibility.\n\nBuild infrastructure supporting shadow testing, A/B testing, fallback strategies and kill switches for safe ML deployment.\n\nSupport the labelling, curation and ongoing development of golden datasets used for model evaluation.\n\nBuild structured human-in-the-loop feedback pipelines to capture reviews and corrections and improve ML datasets.\n\nIntegrate third-party AI APIs and build appropriate adapter/API interfaces.\n\nImplement observability and telemetry covering model behaviour, errors, compute costs, token usage and latency.\n\nContribute backend engineering capability required to integrate ML components reliably into the wider application.\n\nSupport both batch and real-time ML workloads as the platform develops.\n\nRequirements\n6+ years of professional AI/Machine Learning experience, with genuine production experience.\n\n5+ years of professional MLOps experience.\n\nAt least 2+ years of real Applied AI experience, working with AI/ML capabilities beyond experimentation or personal projects.\n\nStrong professional Python experience; Python is the core programming language for this role.\n\nProven experience productionising and deploying AI/ML applications and models.\n\nStrong understanding of both Applied AI/ML and MLOps, rather than experience limited solely to model research or experimentation.\n\nStrong hands-on experience with model evaluation and defining appropriate quality/performance criteria for production ML systems.\n\nExperience working with generative AI/LLMs and understanding evaluation considerations such as grounding and hallucination.\n\nHands-on understanding of RAG and other Applied AI techniques.\n\nExperience building and operating ML pipelines and production ML architectures.\n\nExperience designing reliable ML workflows with appropriate error handling, retry mechanisms and repeatable execution.\n\nExperience working with golden datasets and using them for model evaluation and quality gating.\n\nExperience building observable ML systems using appropriate logging, monitoring and telemetry.\n\nUnderstanding of model/data provenance, auditability and reproducibility.\n\nExperience implementing safe production deployment practices for ML systems, including appropriate testing, fallback or fail-safe mechanisms.\n\nSufficient backend engineering experience to build APIs, integrations and production-ready services around ML capabilities.\n\nExperience solving real production ML problems, including reliability, deployment, integration, evaluation or performance challenges.\n\nFamiliarity with governance, compliance and safeguards relating to sensitive data and AI-generated outputs.\n\nNice to Have\nExperience with FastAPI for building Python-based ML APIs.\n\nExposure to Argo Workflows or similar DAG-based orchestration frameworks.\n\nExperience with Docker and Kubernetes.\n\nExperience working with one or more major cloud platforms: AWS, Azure or GCP.\n\nMulti-cloud or cloud-agnostic application experience.\n\nExperience or understanding of TypeScript and/or Go.\n\nProduction experience with speech-to-text or transcription models.\n\nExperience working with real-time ML applications.\n\nExperience with traditional NLP models, transformer-based models, encoders and decoders.\n\nExperience integrating external models/providers such as OpenAI or 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now","p_open":1,"p_active":0.903,"p_room":1,"age_days":5,"expected_fill_days":26,"reasons":["conf:2","velocity","win:early"],"computed_at":"2026-10-13T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/smartworkingsolutions-sr-machine-learning-engineer-remote-contract-hr216-pk","json_url":"https://alion.io/job/smartworkingsolutions-sr-machine-learning-engineer-remote-contract-hr216-pk.json","meta":{"generated_at":"2026-10-13T15:26:04Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per 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