{"id":1266578,"url":"https://alion.io/job/newton-school-applied-ai-engineer","title":"Applied AI Engineer - LangChain/LangGraph","company":{"id":2237,"name":"Newton School","domain":"newtonschool.co","url":"https://alion.io/company/newton-school","size_band":null,"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":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":11500,"max_usd":28000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":12},"experience_years_min":1,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anthropic","optional":false},{"name":"AutoGen","optional":false},{"name":"Chroma","optional":false},{"name":"Django","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Git","optional":false},{"name":"Hugging Face","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Milvus","optional":false},{"name":"OpenAI","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Weaviate","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"AWS","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Azure","optional":true},{"name":"BentoML","optional":true},{"name":"DeepEval","optional":true},{"name":"Edge AI","optional":true},{"name":"Fine-tuning","optional":true},{"name":"GCP","optional":true},{"name":"LoRA","optional":true},{"name":"MLFlow","optional":true},{"name":"PEFT","optional":true},{"name":"QLoRA","optional":true},{"name":"Ragas","optional":true},{"name":"Transformers","optional":true},{"name":"Vertex AI","optional":true},{"name":"vLLM","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-09-03T11:27:11Z","employer_posted_date":null,"last_verified_at":"2026-09-03T11:27:11Z","board_verified":false,"closed_at":null,"days_open":44,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":44},"description":"About the Role:\n\nWe are seeking an Applied AI Engineer to bridge the gap between state-of-the-art machine learning models and scalable production software. In this role, you will design, build, and deploy production-grade AI solutions, leveraging Large Language Models (LLMs), Generative AI, and modern ML engineering pipelines to solve real-world customer problems.\n\nKey Responsibilities:\n\n- Integrate LLMs, foundational models, and custom ML pipelines into core software products.\n\n- Build, optimize, and maintain RAG (Retrieval-Augmented Generation) architectures, vector search indexes, and autonomous agent workflows.\n\n- Optimize model inference for latency, cost, and throughput via caching, quantization, and batching strategies.\n\n- Collaborate with product and backend teams to build RESTful APIs and microservices powering AI features.\n\n- Establish evaluation frameworks, monitoring, and guardrails to ensure model output accuracy, safety, and reliability in production.\n\nRequired Qualifications:\n\n- Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field.\n\n- Min 1 year of software engineering experience with direct production exposure to AI/ML applications.\n\n- Strong proficiency in Python and backend development frameworks (e.g., FastAPI, Flask, or Django).\n\n- Hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex, or AutoGen) and APIs (OpenAI, Anthropic, Hugging Face).\n\n- Practical experience working with vector databases (Pinecone, Qdrant, Milvus, Weaviate, or Chroma).\n\n- Solid understanding of software engineering fundamentals: Git, Docker, REST APIs, and database design.\n\nGood to Have:\n\n- Experience fine-tuning LLMs using techniques like PEFT, LoRA, or QLoRA.\n\n- Exposure to MLOps tools and platforms (MLflow, Weights & Biases, BentoML, or vLLM).\n\n- Familiarity with cloud services (AWS Bedrock/SageMaker, GCP Vertex AI, or Azure OpenAI).\n\n- Experience with AI evaluation and benchmarking tools (e.g., Ragas, DeepEval).\n\nWhat We Offer:\n\n- Competitive salary package with equity options.\n\n- Budget for cloud compute resources and access to cutting-edge AI hardware/APIs.\n\n- Fast-tracked career growth with direct ownership of high-impact AI products.\n\n- Flexible work environment, health insurance, and learning stipend for AI certifications and conferences.\nSkills\nMachine Learning, Artificial Intelligence, Python, LangChain, Deep Learning, LangGraph, Generative AI, RAG","description_format":"text","description_chars":2451,"description_truncated":false,"requirements":{"experience_years_min":1,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity","Flexible schedule","Health insurance"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["EdTech Platforms","Bootcamps"],"lifecycle":[{"event":"open","at":"2026-09-25T22:00:00Z"}],"visa":[],"liveness":{"score":8,"band":"cold","label":"Long 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