{"id":3513235,"url":"https://alion.io/job/tech-mahindra-tech-lead-software-engineer-41","title":"Tech Lead - Software Engineer","company":{"id":5484,"name":"Tech Mahindra","domain":"techmahindra.com","url":"https://alion.io/company/tech-mahindra","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"lead","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":19500,"max_usd":49000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":74},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"BGP","optional":false},{"name":"CI/CD","optional":false},{"name":"Django","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Function Calling","optional":false},{"name":"Go","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"MLFlow","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series","optional":false},{"name":"VPN","optional":false},{"name":"XGBoost","optional":false},{"name":"FAISS","optional":true},{"name":"Milvus","optional":true},{"name":"Pinecone","optional":true}],"status":"live","first_seen_at":"2026-10-30T09:01:01Z","employer_posted_date":"2026-10-30","last_verified_at":"2026-10-30T17:56:20Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Job Description\n\nSkill Set :\nAI / ML / DL\n\nTotal Experience :\n7.00 to 10.00 Years\n\nNo of Openings :\n1\n\nJob Post Date :\n21/09/2026\n\nJob Expiry Date :\n18/11/2026\n\nDomain :\nIT\n\nLocation :\nBENGALURU [India]\n\nJob Reference No :\n4108186\n\nJob Summary\nRole Title :AI Developer / AI ML Engineer\nSeniority\nMid Level Engineer / Senior Engineer / Applied AI Engineer\nExperience Required\nSenior Level\n•5 to 8+ years overall engineering experience\n•3+ years in AI/ML, GenAI, RAG, MLOps, or intelligent automation\nRole Summary\nNetworking knowledge is important.This is a key differentiator for the team.\nWe are not looking for CCNA level certification as a mandatory requirement, but candidates should have a practical understanding\nof networking concepts relevant to the Crosswork domain, including:\nBGP fundamentals\nRouting\nVPN concepts\nNetwork/transport layers\nGeneral networking architecture\nWe are looking for AI Developers and AI/ML Engineers to build intelligent agents, RAG pipelines, ML models, tool integrations, telemetry analytics, and AI powered network automation capabilities.\nThis role may include both GenAI engineering and applied ML responsibilities. Engineers will work on network troubleshooting agents, configuration drift detection, predictive telemetry analytics, anomaly detection, risk scoring, remediation recommendation, and AI assisted NOC workflows.\nKey Responsibilities\n•Build AI agents for network troubleshooting, configuration drift analysis, risk detection, incident enrichment, RCA support, and remediation recommendations.\n•Develop RAG pipelines using network documentation, topology data, configuration standards, historical incidents, change records, runbooks, and troubleshooting guides.\n•Implement tool calling integrations with telemetry systems, log platforms, topology services, network controllers, ticketing systems, and configuration repositories.\n•Build ML models for anomaly detection, time series prediction, risk scoring, failure prediction, service degradation detection, and topology aware correlation.\n•Process telemetry, logs, events, alarms, metrics, traces, topology, device state, and configuration data.\n•Develop feature engineering pipelines for time series, graph, behavioral, and event based network data.\n•Implement APIs, backend services, data pipelines, prompt workflows, retrieval logic, and model inference services.\n•Work with AI Tech Lead to implement reusable components and follow engineering standards.\n•Collaborate with QA engineers to create AI evaluation datasets and test scenarios.\n•Monitor model, agent, and RAG performance in production and support continuous improvement.\n•Implement observability for model outputs, agent traces, retrieval quality, latency, failures, and tool execution.\nRequired Skills\n•Strong hands on software engineering background in Java, Python, and/or Go.\n•Experience with AI/ML libraries such as scikit learn, PyTorch, TensorFlow, XGBoost, Pandas, NumPy, or similar.\n•Experience with GenAI concepts such as RAG, embeddings, vector search, prompt engineering, LLM APIs, tool/function calling, and response grounding.\n•Experience with backend/API development using FastAPI, Flask, Django, or equivalent.\n•Experience with SQL, NoSQL, object storage, data pipelines, and event processing.\n•Understanding of ML model lifecycle including training, evaluation, deployment, monitoring, and retraining.\n•Familiarity with anomaly detection, classification, clustering, time series analysis, and model evaluation.\n•Basic understanding of enterprise system design including scalability, error handling, logging, monitoring, and secure API integration\n•Experience with Docker, Kubernetes, CI/CD, MLflow, model registry, and MLOps pipelines.\nPreferred Skills\n•Experience with network telemetry, logs, alarms, topology, service assurance, or NOC workflows.\n•Experience with vector databases such as FAISS, Milvus, 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