{"id":1227102,"url":"https://alion.io/job/tredence-inc-senior-artificial-intelligence-engineer","title":"Senior Artificial Intelligence Engineer","company":{"id":1263,"name":"Tredence Inc","domain":"tredence.com","url":"https://alion.io/company/tredence-inc","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","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":47000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Arize Phoenix","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Kubernetes","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Node JS","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Transformers","optional":false},{"name":"TypeScript","optional":false},{"name":"Apache Kafka","optional":true},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"DSPy","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Flink","optional":true},{"name":"GraphRAG","optional":true},{"name":"Hadoop","optional":true},{"name":"JavaScript","optional":true},{"name":"LangChain","optional":true},{"name":"Machine Learning","optional":true},{"name":"Multimodal AI","optional":true},{"name":"Neo4j","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Qdrant","optional":true},{"name":"Spark","optional":true},{"name":"Tool Use","optional":true},{"name":"Voice Agents","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-09-25T11:54:49Z","employer_posted_date":null,"last_verified_at":"2026-09-25T11:54:49Z","board_verified":false,"closed_at":null,"days_open":6,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":6},"description":"Job Description :\n\nAs an AI Engineer focused on Production AI Agents, you will partner closely with Product, Research, Engineering, and cross-functional stakeholders to design, build, and scale AI-powered systems that enhance how insights are generated and operationalized. This role emphasizes moving beyond experimentation to deliver reliable, evaluation-driven AI solutions that integrate seamlessly into real workflows. You will play a key role in shaping GCI's AI ecosystem by building robust agent architectures, ensuring production readiness, and continuously improving system performance and trust.\n\nWhat you will accomplish :\n\n- Design and build stateful, multi-agent AI systems using modern orchestration frameworks, enabling scalable and reliable workflows for insights generation and synthesis.\n\n- Collaborate with Product, Research, and business stakeholders to translate requirements into end-to-end AI solutions, from proof of concept through evaluation and production deployment.\n\n- Solid understanding of Retrieval-Augmented Generation (RAG), including hybrid search, re-ranking, and advanced retrieval techniques.\n\n- Implement evaluation frameworks and pipelines (e.g., LLM-as-a-Judge, automated benchmarks) to measure system performance, reliability, and quality before and after release.\n\n- Develop and maintain scalable backend services for high-throughput, low-latency workloads, while contributing to lightweight frontend components to deliver functional prototypes and internal tools.\n\n- Optimize batching, streaming, caching, and request orchestration in distributed and async environments.\n\n- Improve production systems across latency, throughput, reliability, observability, and unit economics.\n\n- Partner with infrastructure teams to leverage GPU-enabled and cloud-native environments effectively.\n\n- Establish monitoring, tracing, and observability practices for complex AI systems, ensuring performance, reliability, and debuggability in production.\n\n- Develop reusable platform components, MCPs / APIs, and best practices for AI application development.\n\n- Drive a pragmatic, evaluation-driven approach to adopting new AI technologies, balancing innovation with reliability and business impact.\n\n- Stay current with advancements in AI (e.g., reasoning models, SLMs, prompting strategies) and apply them to improve systems and workflows.\n\n- Partner with cross-functional teams to ensure AI solutions align with responsible AI, privacy, and security standards.\n\nWhat you will bring :\n\n- 8 - 12 years of experience in software engineering, AI/ML engineering, or full-stack development, with hands-on ownership of building and deploying production-grade applications or platforms.\n\n- 6+ years of focused experience building and deploying AI-centric systems.\n\n- 4+ years of hands-on experience with LLM-based agents, autonomous workflows, or multi-agent orchestration.\n\n- Strong full-stack engineering experience, with deep expertise in Python and familiarity with TypeScript or Node.js.\n\n- Hands-on experience with AI orchestration frameworks such as LangGraph, LlamaIndex Workflows, or similar tools.\n\n- Solid understanding of Retrieval-Augmented Generation (RAG), including hybrid search, re-ranking, and advanced retrieval techniques.\n\n- Experience implementing observability and tracing for AI systems (e.g., LangSmith, LangFuse, Arize Phoenix).\n\n- Production experience with modern ML tooling and frameworks (for example: PyTorch, Transformers, scikit-learn).\n\n- Proven experience taking AI-powered products from prototype to production with strong maintainability and operational quality.\n\n- Proven ability to design and execute evaluation pipelines and testing frameworks to ensure reliability and reduce hallucinations.\n\n- Experience working with APIs/SDKs from major model providers (OpenAI, Anthropic, Gemini) and open-source models.\n\n- Experience deploying and managing services on cloud platforms (AWS, Azure, or GCP) and using containerization (Docker/Kubernetes).\n\n- Familiarity with CI/CD pipelines and DevOps practices.\n\n- Strong collaboration and communication skills, with the ability to work effectively across technical and non-technical teams.\n\nPreferred Qualifications :\n\n- Experience with Spring-based service development.\n\n- Familiarity with big data and processing ecosystems (for example: Spark, Hadoop).\n\n- Experience with streaming systems (for example: Kafka, Flink, Beam).\n\n- Experience with RAG pipelines, vector stores, tool-use frameworks, and multimodal model integration.\n\n- Exposure to GPU optimization and performance tuning (for example: CUDA, inference optimization techniques).\n\n- Experience building conversational AI systems (intents, entities, dialog flows, and interaction design).\n\n- Familiarity with prompt optimization tools such as DSPy.\n\n- Proficiency with vector databases (Pinecone, Weaviate, Qdrant, pgvector).\n\n- Exposure to voice agents or multimodal AI systems.\n\n- Experience with graph databases (e.g., Neo4j) or GraphRAG approaches.\n\n- Foundational knowledge of machine learning or model fine-tuning.\nSkills\nArtificial Intelligence, Agentic AI, RAG, LLM, Machine Learning, Python, LangGraph, AWS, PyTorch","description_format":"text","description_chars":5188,"description_truncated":false,"requirements":{"experience_years_min":8,"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":["AI Consulting & Integration","Analytics & BI Consulting"],"lifecycle":[{"event":"open","at":"2026-09-25T13:06:44Z"}],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":5,"expected_fill_days":42,"reasons":["seen:5","velocity","win:early"],"computed_at":"2026-10-01T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/tredence-inc-senior-artificial-intelligence-engineer","json_url":"https://alion.io/job/tredence-inc-senior-artificial-intelligence-engineer.json","meta":{"generated_at":"2026-10-01T20:39:50Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3094,"day_limit":5000,"remaining_today":1906,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}