{"id":1252838,"url":"https://alion.io/job/guddge-tech-mlops-engineer","title":"MLOps Engineer","company":{"id":3806677,"name":"Guddge Tech","domain":"guddge.com","url":"https://alion.io/company/guddge-tech","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":20000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":25},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon EventBridge","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"Boto3","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"flake8","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"GitHub Actions","optional":false},{"name":"IAM","optional":false},{"name":"isort","optional":false},{"name":"Least Privilege","optional":false},{"name":"Machine Learning","optional":false},{"name":"pySpark","optional":false},{"name":"Pytest","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"SAP HANA","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"SHAP","optional":false},{"name":"Spark","optional":false},{"name":"Terraform","optional":false},{"name":"Windows","optional":false}],"status":"live","first_seen_at":"2026-09-12T06:20:01Z","employer_posted_date":null,"last_verified_at":"2026-09-12T06:20:01Z","board_verified":false,"closed_at":null,"days_open":34,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":34},"description":"About the Role :\n\nWe're looking for an MLOps Engineer to support and evolve a production machine-learning platform that produces daily risk scores used by operational leaders to intervene before incidents occur. You'll own the reliability, deployment, and monitoring of a production ML pipeline that spans AWS (S3, Glue, SageMaker, Step Functions), and a fully automated Terraform + GitHub Actions delivery pipeline across multi environments.\n\nThis is a hands-on infrastructure-and-operations role. The model itself is trained offline; your focus is keeping the scoring pipeline dependable, observable, secure, and easy to change.\n\nWhat You'll Do :\n\n- Operate and improve the end-to-end inference pipeline (SAP HANA - AWS Glue - AWS SageMaker - AWS Step Functions - SAP HANA), keeping scheduled daily runs reliable.\n\n- Own the infrastructure-as-code: maintain Terraform modules and configuration, manage remote state in Terraform Cloud, and promote changes through dev - qa - prd.\n\n- Maintain and extend the CI/CD workflows in GitHub Actions.\n\n- Keep the pipeline observable: structured JSON logging, CloudWatch Logs Insights queries, and Step Functions execution monitoring.\n\n- Support model monitoring (drift detection) and data-quality checks; tune alert thresholds and triage failure alerts.\n\n- Troubleshoot production incidents using established runbooks (database connectivity, SageMaker job failures, Glue write-back, scheduler issues) and drive root-cause fixes.\n\n- Enforce security and compliance controls: encryption, least-privilege IAM, secrets handling, and no-PII-in-logs discipline.\n\n- Collaborate with data scientists to deploy retrained models through the SageMaker Model Registry and cross-account promotion process.\n\nRequired Qualifications :\n\n- 3+ years in MLOps supporting production systems.\n\n- Strong AWS experience, especially the data/ML services.\n\n- Production Terraform experience with a remote backend and multi-environment promotion.\n\n- Solid Python for data processing and operational scripting.\n\n- Experience operating CI/CD pipelines and diagnosing production failures from logs and metrics.\n\nTechnical Skillset Breakdown :\n\nThe percentages reflect the relative weight of each area for day-to-day success in this role.\n\n1. AWS Cloud & ML Services: ~35% :\n\nThe core of the platform runs on AWS.\n\n- AWS Glue - Spark/PySpark ETL jobs, Glue connections (VPC/JDBC), Data Catalog, crawlers, job bookmarks.\n\n- Amazon SageMaker - Processing Jobs, Model Registry, cross-account model packages, execution roles.\n\n- AWS Step Functions - orchestration, Amazon States Language (ASL), retry/error handling.\n\n- Amazon EventBridge Scheduler - cron scheduling, timezone handling.\n\n- Amazon S3 - bucket policies, versioning, server-side encryption, lifecycle.\n\n- Amazon CloudWatch - Logs, Logs Insights, monitoring.\n\n- AWS Lake Formation - fine-grained catalog access control.\n\n- VPC / Security Groups / Prefix Lists - network connectivity to on-prem.\n\n2. Infrastructure as Code (Terraform): ~20% :\n\nNearly all infrastructure is managed as code.\n\n- Terraform module authoring and reuse.\n\n- Terraform Cloud (remote state, workspaces, locking).\n\n- Multi-environment configuration (.tfvars, per-env config files).\n\n- Provider configuration (AWS, AWSCC).\n\n- Managing drift, plan/apply safety, state troubleshooting.\n\n3. CI/CD & Automation: ~15% : \n\nDelivery is fully automated.\n\n- GitHub Actions: workflow authoring, reusable composite actions, environment approvals.\n\n- Sequential environment promotion (dev - qa - prd).\n\n- Git and branch/PR workflows, GitHub CLI / AWS Kiro.\n\n- Make for build automation.\n\n4. Python & ML Tooling: ~10% :\n\nSupporting the model code and tests.\n\n- Python 3.8+ for inference/ETL scripts and operational tooling.\n\n- pandas, pyarrow, scipy for data processing.\n\n- boto3 for AWS automation.\n\n- SHAP (model explainability), imbalanced-learn (SMOTE) - familiarity to support the model.\n\n- Scikit-learn model artifacts (Random Forest, MinMaxScaler) and pickle handling.\n\n- pytest + coverage (50% gate), Black, flake8, isort.\n\n5. Docker & Containerization: ~10% :\n\nContainers underpin local testing and reproducible builds.\n\n- Building and running Docker images for development and CI.\n\n- Containerized test execution (e.g., PySpark unit tests that don't run natively on Windows).\n\n- Managing dependencies and reproducible environments across local and pipeline runs.\n\n- Familiarity with container-based execution in AWS (Glue/SageMaker managed containers).\n\n6. Observability, Reliability & Security: ~10% :\n\nKeeping it production-grade.\n\n- Structured JSON logging and a typed error hierarchy (config/data/model/inference/downstream).\n\n- Model drift and data-quality monitoring; alert threshold tuning.\n\n- Incident triage using runbooks; alerting via SNS/SES dispatcher integration.\n\n- Security controls: encryption in transit/at rest, least-privilege IAM, no PII in logs.\n\n- Data-quality validation.\n\nWorking Environment :\n\n- This is an offshore role, you will be working from your base location. No travel required.\n\n- Ability to work US Pacific Standard Time Zone (i.e. 8 pm IST to 5 am IST).\nSkills\nTerraform, AWS SageMaker, AWS Glue, Python, MLOps, Machine Learning, Cloud Architect - AI/ML, AI Observability, CI/CD Tools","description_format":"text","description_chars":5260,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-25T18:04:06Z"}],"visa":[],"liveness":{"score":14,"band":"cold","label":"Long shot","p_open":0.6,"p_active":0.533,"p_room":0.45,"age_days":33,"expected_fill_days":20,"reasons":["seen:33","win:tail"],"computed_at":"2026-10-16T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/guddge-tech-mlops-engineer","json_url":"https://alion.io/job/guddge-tech-mlops-engineer.json","meta":{"generated_at":"2026-10-17T03:47:25Z","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 call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":4986,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-18T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":3806677},"rest":"https://alion.io/mcp/rest/get_company?id=3806677"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fguddge-tech-mlops-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fguddge-tech-mlops-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fguddge-tech-mlops-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/guddge-tech-mlops-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fguddge-tech-mlops-engineer"}]}