{"id":3016885,"url":"https://alion.io/job/soliton-technologies-pvt-ltd-python-ai-engineer","title":"Python AI Engineer","company":{"id":2388332,"name":"Soliton Technologies Pvt Ltd","domain":"solitontech.com","url":"https://alion.io/company/solitontech","size_band":"201-500","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":"middle","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","Chennai, India","Coimbatore, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19000,"max_usd":50000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":31},"experience_years_min":3,"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":"Context Engineering","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"GCP","optional":false},{"name":"Hugging Face","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LangChain","optional":false},{"name":"Llama","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Mistral","optional":false},{"name":"MLFlow","optional":false},{"name":"Ollama","optional":false},{"name":"OpenAI","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"RAG","optional":false},{"name":"TruLens","optional":false},{"name":"Weaviate","optional":false},{"name":"Fine-tuning","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Multimodal AI","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-10-21T16:35:45Z","employer_posted_date":null,"last_verified_at":"2026-10-21T16:35:45Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"We are seeking an Applied AI Engineer to design, build, and deploy intelligent applications leveraging Generative AI, Large Language Models (LLMs), and modern AI engineering practices.\n\nThe Applied AI Engineer is responsible for building AI-driven applications by integrating Generative AI models, data pipelines, and inference systems into production-ready solutions. This role involves hands-on development with Python and AI frameworks, designing RAG-based systems, optimising model performance, and ensuring responsible AI deployment through guardrails and observability.\n\nResponsibilities: \nDesign, implement, and optimise Generative AI applications using Python and frameworks such as FastAPI.\nBuild AI solutions using LLM frameworks like LlamaIndex and LangChain.\nImplement containerised deployments using Docker.\nDevelop and optimise Retrieval-Augmented Generation (RAG) pipelines for improved information retrieval.\nWork with self-hosted and cloud-based vector databases for efficient search and retrieval.\nDesign and manage knowledge graphs and graph-based RAG systems.\nImplement re-ranking models and retrieval optimisation techniques.\nApply prompt engineering and context engineering to enhance model performance.\nEstablish guardrails to ensure safe, ethical, and compliant AI deployments.\nBuild data preprocessing and transformation pipelines for structured and unstructured data.\nPerform inference using offline LLMs via platforms like Ollama or Hugging Face (Llama, Mistral).\nIntegrate online LLM providers such as OpenAI, Anthropic, or GCP for real-time inference.\nMonitor AI workflows using observability tools like MLflow or Arize Phoenix.\nEvaluate model performance using frameworks such as TruLens or custom-built evaluation systems.\nContinuously improve AI systems based on evaluation insights, metrics, and user feedback.\n\nRequirements: \nExperience building Generative AI applications using Python and FastAPI.\nHands-on knowledge of LLM frameworks such as LangChain or LlamaIndex.\nAbility to work with unstructured data (PDFs, documents, chunking, search) and structured data.\nExperience designing RAG-based systems, including prompt engineering and retrieval optimisation.\nFamiliarity with vector databases (Qdrant, Pinecone, Weaviate) and search solutions.\nExposure to AI agents, workflows, and basic orchestration concepts.\nExperience using cloud platforms like Azure or AWS.\nWorking knowledge of online and offline LLMs (OpenAI, Llama, Mistral).\nUnderstanding of AI evaluation, monitoring, and observability concepts.\nExperience with Docker and CI/CD pipelines for deploying AI applications.\n\nGood to Have: \nExperience with MCP clients and servers.\nKnowledge of multimodal LLMs for image and voice processing.\nKnowledge of deploying applications in cloud or on-prem infrastructure.\nKnowledge of fine-tuning techniques and data preparation for fine-tuning.\n\nQualifications: \nBachelor's or Master's degree in Computer Science, Engineering, or a related field.\nProven experience in AI/ML engineering and related technologies.\n3+ years of experience building applications using Python and asynchronous programming.\nExperience working with SQL and NoSQL databases.\nStrong problem-solving skills and ability to work in a fast-paced environment.\nExcellent communication and teamwork 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