{"id":3387816,"url":"https://alion.io/job/teradata-staff-software-engineer-cloud-ai-platform","title":"Staff Software Engineer - Cloud & AI Platform","company":{"id":58848,"name":"Teradata","domain":"teradata.com","url":"https://alion.io/company/teradata","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Backend","role_family":"Backend","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":32000,"max_usd":78000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":26},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Blue-Green Deployment","optional":false},{"name":"CI/CD","optional":false},{"name":"GCP","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-10-28T06:48:07Z","employer_posted_date":null,"last_verified_at":"2026-10-28T06:48:07Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Job Description :\n\nWe are looking for an exceptional Staff Cloud & AI Platform Engineer to own and independently build a next-generation enterprise-scale deployment and orchestration platform.\n\nThis role goes far beyond traditional CI/CD or infrastructure automation - it sits at the intersection of distributed systems, control-plane architecture, SRE principles, and applied AI/agentic workflows.\n\nContribute to the design and implementation of a stateful deployment control plane that treats deployments as a continuous convergence problem.\n\nEnable safe, resumable, deterministic, and SLO-governed rollouts across multi-cloud, on-prem, and customer environments.\n\nImplement the practical application of AI agents in DevOps by embedding intelligent decision-making into deployment workflows.\n\nApply AI agents for anomaly detection, failure diagnosis, rollout control, and automated mitigation.\n\nEnsure all AI-assisted workflows preserve correctness, determinism, and full auditability.\n\nThis is a high-impact individual contributor role requiring deep technical judgment, delivery ownership, and the ability to turn abstract design into production-grade systems.\n\nKey Responsibilities :\n\n- Design and implement a stateful deployment control plane with deterministic, resumable execution and rollback support.\n\n- Build deployment workflows that reconcile desired vs. actual state using durable control loops.\n\n- Support multi-cloud, on-prem, and customer environments with a single orchestration framework.\n\n- Implement SLO-governed promotion gates, error-budget checks, and bounded retry policies.\n\n- Enable deployment models including self-service, wave-based rollouts, blue-green, managed updates, and maintenance windows.\n\n- Define declarative, extensible workflow schemas with explicit dependencies, retries, and rollback semantics.\n\n- Integrate deployment orchestration with CI/CD systems, artifact manifests, entitlement services, and downstream execution systems.\n\n- Embed AI-assisted decision logic into deployment workflows for anomaly detection, failure diagnosis, and rollout control.\n\n- Ensure AI-driven actions remain deterministic, auditable, reversible, and policy-constrained.\n\n- Develop infrastructure and automation frameworks using Terraform, Python, and cloud-native services.\n\n- Embed observability and telemetry into workflows to drive automated validation, retries, and rollback decisions.\n\n- Provide hands-on technical leadership, mentoring engineers and driving platform architecture across DevOps, SRE, and CloudOps teams.\n\nWho You'll Work With :\n\nThis is an individual contributor role. You will collaborate with project team members and architects, and report to the Senior Engineering Manager.\n\nWhat Makes You a Qualified Candidate :\n\n- Bachelor's or Master's degree in computer science or related field.\n\n- 8+ years of hands-on experience in DevOps, platform engineering, cloud infrastructure, or distributed systems.\n\n- Proven experience developing and implementation of stateful systems such as control planes, workflow engines, or orchestration platforms.\n\n- Experience building or operating enterprise-scale deployment systems across multi-cloud, hybrid, or on-prem environments.\n\n- Deep expertise in CI/CD, release engineering, and deployment strategies including wave-based, blue-green, and managed rollouts.\n\n- Practical experience applying SRE principles such as SLOs, error budgets, promotion gates, and automated rollback.\n\n- Strong programming skills in Python, with the ability to build long-running services and automation frameworks.\n\n- Hands-on experience with Infrastructure as Code, preferably Terraform, and cloud-native services (AWS, Azure, and/or GCP).\n\n- Experience integrating observability and telemetry (metrics, logs, traces) into automated systems for decision-making.\n\n- Demonstrated experience building or integrating AI-assisted or agentic workflows into DevOps or operational systems.\n\n- Ability to design safe, auditable, policy-constrained automation, especially when applying AI or autonomous decision logic.\n\n- Strong technical judgment and communication skills, with experience leading architecture across teams as an individual contributor.\nSkills\nCloud Infrastructure, CI/CD, IT Infrastructure Automation, Site Reliability, IAC Terraform, AWS, Hybrid Cloud, AI Integration, Azure, Google Cloud 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