{"id":2154314,"url":"https://alion.io/job/credit-saison-senior-ml-engineer","title":"Senior ML Engineer","company":{"id":841,"name":"Credit Saison","domain":"creditsaison.in","url":"https://alion.io/company/credit-saison","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":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":48000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":27},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Claude Code","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Kubeflow","optional":false},{"name":"LIME","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"ONNX","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SHAP","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-10-09T06:14:58Z","employer_posted_date":null,"last_verified_at":"2026-10-09T06:14:58Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"We are looking for an ML Engineer/Senior ML Engineer who enjoys taking machine learning systems beyond experimentation and into high-scale, production environments. You will work closely with data scientists and engineering teams to build real-time ML infrastructure, high-throughput inference systems, feature pipelines, model serving platforms, and production observability. This is an engineering-heavy ML role for someone who enjoys solving problems at the intersection of machine learning, distributed systems, platform engineering, and MLOps.\nResponsibilities:\nDesign and build high-throughput, low-latency real-time and batch feature computation pipelines.\nPartner with data scientists to deploy, profile, optimize, and scale complex ML models for production inference workloads.\nBuild end-to-end ML observability, including tracing, monitoring, alerting, and production dashboards.\nOwn technical integrations for ML-powered data products and real-time decisioning systems.\nDefine and optimize infrastructure for scalable ML deployments.\nArchitect systems supporting advanced model deployment patterns such as ensembles, model chaining, shadow deployments, and champion-challenger frameworks.\nWork across internal and external datasets to build impactful ML solutions.\nApply modern AI/ML frameworks to solve critical business problems.\nRequirements:\n2-5 years of hands-on experience deploying machine learning models in production, preferably real-time environments.\nBachelor's / Master's / PhD in a STEM discipline from a top-tier institution.\nStrong fundamentals in machine learning, neural networks, and deep learning.\nStrong understanding of distributed systems, real-time computing, and inference-serving architectures.\nStrong programming skills in Python and SQL.\nExposure to Databricks is highly advantageous.\nStrong software engineering fundamentals and an emphasis on production-quality code.\nComfortable using modern developer tools, including AI coding assistants such as Claude Code / Codex.\nHands-on exposure to:\nModel Serving: TensorFlow Serving, ONNX.\nCloud: AWS ecosystem.\nMLOps: MLflow, Kubeflow.\nContainers: Docker.\nML: PyTorch, scikit-learn.\nExplainability: SHAP, LIME.\nDistributed Processing: Apache Spark.\nStrong Plus:\nExperience building ML systems in financial services, fintech, lending, or credit.\nUnderstanding of 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