{"id":1736433,"url":"https://alion.io/job/ericsson-agentic-ai-developer","title":"Agentic AI Developer","company":{"id":56456,"name":"Ericsson","domain":"ericsson.com","url":"https://alion.io/company/ericsson","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"B","score":84,"open_postings":50,"ghost_share":0,"stale_share":0.66,"repost_share":0,"time_to_fill_p50_days":53,"computed_at":"2026-10-05T05:45:15Z"}},"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":["Noida, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":20000,"max_usd":42000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":28},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AI Agents","optional":false},{"name":"Apache Kafka","optional":false},{"name":"ArgoCD","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"CrewAI","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Flask","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google ADK","optional":false},{"name":"gRPC","optional":false},{"name":"Hugging Face","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Java","optional":false},{"name":"Jenkins","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LoRA","optional":false},{"name":"Milvus","optional":false},{"name":"MLFlow","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"ONNX","optional":false},{"name":"Pandas","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Polars","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Qdrant","optional":false},{"name":"QLoRA","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"RLHF","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorRT","optional":false},{"name":"Transformers","optional":false},{"name":"Vertex AI","optional":false},{"name":"Weaviate","optional":false},{"name":"Weights & Biases","optional":false},{"name":"LangChain","optional":true},{"name":"PEFT","optional":true},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-08-18T12:16:17Z","employer_posted_date":"2026-09-28","last_verified_at":"2026-10-05T23:39:10Z","board_verified":true,"closed_at":null,"days_open":48,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":48},"description":"Join our Team\nAbout the opportunity:\nEricsson’s Service Architecture team, within Service Management in the Operate Service Line, is building intelligent automation and decision-support systems for telecom-scale managed services. In Software Development, you will design, build, test, and ship production-grade AI services focused on Agentic AI, LLM integrations, vector retrieval, knowledge graph reasoning, and ML infrastructure. Working with platform engineers, data scientists, and product owners, you will turn AI experiments into reliable services that deliver measurable value across Ericsson’s managed service operations.\nWhat you will do:\nDesign multi-agent systems using LangGraph, AutoGen, CrewAI, Google ADK, or equivalent orchestration.\nBuild LLM services with prompt engineering, tool/function calling, planning, memory, reflection, guardrails, fallback patterns, and RAG.\nIntegrate commercial API-based and open-source/self-hosted LLMs,balancing cost,latency, accuracy,privacy and complexity.\nBuild RAG pipelines using ChromaDB,Qdrant,Vertex AI RAG,Pinecone,Weaviate, pgvector, Milvus, or equivalent; manage embeddings, hybrid search, re-ranking, and index lifecycle.\nModel and query property graphs and RDF/ontologies to enrich agent context, semantic reasoning, and structured retrieval.Create evaluation harnesses with automated scoring, human review, A/B testing, and golden datasets.\nDevelop ML pipelines for feature engineering, training, evaluation, serving, fine-tuning, packaging, versioning, and monitoring using Python, PyTorch, scikit-learn, and Hugging Face.\nApply ML methods including regression, classification, tree ensembles, boosting, SVMs, neural networks, clustering, PCA/UMAP, LSTMs, and Transformers.\nMonitor model drift, data quality, performance, latency, and token cost in production.Build clean, testable Python/PySpark services and REST/FastAPI/Flask/gRPC APIs using clean-architecture principles.\nContribute to CI/CD with GitHub Actions, Jenkins, or ArgoCD; participate in reviews, architecture discussions, documentation, runbooks, and mentoring.\nYou will bring:\n6 -15 years of software development experience and a Bachelor’s/master’s degree in computer science, Software Engineering, AI/ML, or equivalent.\n5+ years of hands-on ML engineering and LLM development experience in production.\nProduction experience with Agentic AI, vector database-backed RAG, and knowledge graph integrations.\nExpert Java Backend/Full stack Development and Python Scripting, Microservices Architecture, API’s, Service Design, Design Patterns Experience with Spark, SQL, Pandas/Polars, structured streaming, Delta Lake/Iceberg, and Kafka/Pulsar.\nLLM fine-tuning and optimisation knowledge, including LoRA/QLoRA, RLHF, ONNX, TensorRT, quantisation, and efficient inference.\nExperience with Docker, Kubernetes, AWS/Azure/GCP, Databricks or EMR, and MLOps tools such as MLflow or Weights & Biases.\nKnowledge of LLM observability, structured logging, token-cost tracking, alerting, responsible AI, and model safety.\nWorking knowledge of TOGAF or an equivalent enterprise architecture framework and technical documentation standards.\nNote- All academic credentials must be from recognized and accredited institutions and are further subject to verification.\nWhat happens once you apply?Click Here to find all you need to know about what our typical hiring process looks like.We encourage you to consider applying to jobs where you might not meet all the criteria. We recognize that we all have transferrable skills, and we can support you with the skills that you need to develop.Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. 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