{"id":29192,"url":"https://alion.io/job/salvo-software-ai-developer","title":"AI Developer","company":{"id":7930,"name":"Salvo Software","domain":"salvosoftware.com","url":"https://alion.io/company/salvo-software","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":96,"open_postings":4,"ghost_share":0,"stale_share":0,"repost_share":0.25,"time_to_fill_p50_days":null,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","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":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AWQ","optional":false},{"name":"Azure DevOps","optional":false},{"name":"Bitsandbytes","optional":false},{"name":"Chroma","optional":false},{"name":"CI/CD","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"cuDNN","optional":false},{"name":"Docker","optional":false},{"name":"FAISS","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GGUF","optional":false},{"name":"Git","optional":false},{"name":"GPTQ","optional":false},{"name":"Hugging Face","optional":false},{"name":"Hybrid Search","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Mistral","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"MySQL","optional":false},{"name":"Ollama","optional":false},{"name":"pgvector","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Qwen","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SFT","optional":false},{"name":"TensorFlow","optional":false},{"name":"TGI","optional":false},{"name":"Tokenization","optional":false},{"name":"Transformers","optional":false},{"name":"vLLM","optional":false},{"name":"Weaviate","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-08-07T00:00:00Z","employer_posted_date":"2026-08-07","last_verified_at":"2026-10-16T04:37:57Z","board_verified":true,"closed_at":null,"days_open":70,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":70},"description":"About Salvo Software\nSalvo Software is a global firm that provides cost-effective software solutions to guide enterprises and startups through digital transformation. With distributed teams across the US, LATAM, and India, we partner with clients to build high-performance, scalable systems that solve complex technical challenges. Our culture values innovation, ownership, and engineering excellence.\nRole Overview\nWe are seeking a highly skilled AI Developer with a strong backend and machine learning engineering background to design, train, optimize, and deploy LLM models in on-prem and offline environments. This role is deeply technical and hands-on, requiring expertise across Python ML stacks, model optimization, local inference frameworks, RAG (Retrieval-Augmented Generation) architectures, MCP (Model Context Protocol) integrations, and DevOps workflows tailored for offline systems.\nYou will work closely with our engineering and product teams to build end-to-end LLM pipelines - including data preprocessing, supervised fine-tuning, model quantization, evaluation, RAG pipeline design, and deployment using local or air-gapped infrastructure. If you enjoy working with cutting-edge open-source LLMs, building context-aware AI systems, and designing reliable backend pipelines, this role is for you.\nKey Responsibilities\nCore LLM Development\nTrain and fine-tune LLMs using supervised fine-tuning (SFT).\nWork with open-source models such as LLaMA, Mistral, Qwen, and similar architectures.\nBuild LoRA / Q-LoRA pipelines for efficient fine-tuning.\nImplement and optimize data preprocessing workflows, including tokenization and long-context handling.\nUse and extend Hugging Face Transformers & Datasets for training and inference.\nParse and process structured and semi-structured data, including XML/XSD files.\nImplement document parsing solutions for Office formats (python-docx, OpenXML).\nRAG & Context-Aware Systems\nDesign and implement end-to-end Retrieval-Augmented Generation (RAG) pipelines for document-grounded question answering and knowledge retrieval.\nBuild and maintain vector stores and embedding pipelines using tools such as FAISS, Chroma, Weaviate, or pgvector.\nOptimize retrieval strategies including hybrid search, re-ranking, and chunking approaches tailored for domain-specific corpora.\nDevelop and maintain MCP (Model Context Protocol) server integrations to enable LLMs to interact dynamically with tools, APIs, and external data sources.\nDesign agentic workflows that leverage MCP to give models structured access to internal systems and context in a controlled, auditable manner.\nOffline / On-Prem Model Expertise\nDeploy, run, and maintain models fully offline and in air-gapped environments.\nPerform model optimization and quantization (GGUF, GPTQ, AWQ, bitsandbytes).\nBuild and maintain inference systems using frameworks like vLLM, TGI, and Ollama.\nOptimize GPU usage (CUDA, cuDNN, VRAM-aware batching).\nMaintain local CI/CD pipelines for ML models without cloud dependencies.\nManage local model registries, versioning, and artifacts.\nEnsure RAG and MCP components are fully operational in offline and restricted network environments.\nBackend & DevOps\nBuild backend services in Python for ML training and inference workflows.\nWork with relational databases (Postgres/MySQL) and vector databases for RAG storage layers.\nUse Docker and Git for reliable development and deployment pipelines.\nUse Azure DevOps for CI/CD, including local runners when applicable.\nRequirements\nTechnical Skills\nStrong experience in Python for backend and ML development.\nExpertise with ML frameworks such as PyTorch or TensorFlow, scikit-learn, and pandas.\nSolid knowledge of Postgres or MySQL for data storage.\nExperience with Docker, Git, and DevOps best practices.\nHands-on expertise with LLM training, fine-tuning, and optimization.\nExperience with Hugging Face Transformers & Datasets.\nFamiliarity with XML/XSD and Office document parsing tools.\nExperience deploying models with vLLM, TGI, or Ollama.\nUnderstanding of quantization techniques (GGUF/GPTQ/AWQ).\nExperience working with GPU optimization and the CUDA stack.\nAbility to build solutions for offline, on-prem, and air-gapped environments.\nHands-on experience designing and implementing RAG pipelines, including embedding models, vector stores (FAISS, Chroma, Weaviate, or pgvector), and retrieval optimization strategies.\nExperience building or integrating MCP (Model Context Protocol) servers to connect LLMs with external tools, APIs, and structured data sources.\nNice to Have\nExperience building agentic systems using MCP in production or near-production environments.\nFamiliarity with advanced RAG techniques such as HyDE, re-ranking, or multi-hop retrieval.\nExperience managing ML model registries in offline environments.\nFamiliarity with AWS for hybrid deployments.\nExperience with secure environments, restricted networks, or enterprise compliance requirements.\nSoft Skills\nStrong ownership mindset and problem-solving ability.\nAbility to work effectively in distributed teams across time zones.\nClear 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