{"id":1252873,"url":"https://alion.io/job/huntingcube-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":3801649,"name":"Huntingcube","domain":"huntingcube.ai","url":"https://alion.io/company/huntingcube","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":true,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["India"],"countries":["IN"],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":19000,"max_usd":49000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":8},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Chroma","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"Hugging Face","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Milvus","optional":false},{"name":"NLP","optional":false},{"name":"PEFT","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"QLoRA","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Rest API","optional":false},{"name":"Transformers","optional":false},{"name":"Arize Phoenix","optional":true},{"name":"DeepSpeed","optional":true},{"name":"FSDP","optional":true},{"name":"LangSmith","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"Recommender Systems","optional":true},{"name":"Semantic Search","optional":true},{"name":"Semantic Search","optional":true},{"name":"Speech Recognition","optional":true},{"name":"TGI","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-09-11T12:48:31Z","employer_posted_date":null,"last_verified_at":"2026-09-11T12:48:31Z","board_verified":false,"closed_at":null,"days_open":18,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":18},"description":"About the Role : \n\nWe are looking for a Machine Learning Engineer to build and deploy production-grade AI systems powered by Large Language Models (LLMs). In this role, you will work on retrieval-augmented generation (RAG), search, multilingual NLP, document understanding, evaluation frameworks, and AI-powered workflows. You will collaborate closely with research, engineering, and product teams to deliver reliable, scalable AI solutions used in real-world environments.\n\nKey Responsibilities : \n\n- Design, build, and deploy production-ready LLM and NLP applications.\n\n- Develop retrieval and search pipelines using RAG, embeddings, vector databases, and reranking techniques.\n\n- Build AI agents and workflow automation for complex business use cases.\n\n- Fine-tune and adapt open-source LLMs using techniques such as LoRA, QLoRA, or PEFT.\n\n- Develop multilingual NLP solutions including summarization, translation, information extraction, and question answering.\n\n- Design evaluation frameworks to measure model quality, hallucinations, factuality, latency, and overall user experience.\n\n- Build feedback pipelines that capture production failures and improve model performance.\n\n- Work closely with data, engineering, and product teams to translate business requirements into scalable ML solutions.\n\n- Deploy and maintain ML models in production while ensuring scalability, monitoring, and reliability.\n\n- Continuously improve system performance, inference efficiency, and operational costs.\n\nRequired Skills : \n\n- 3-6 years of experience building production Machine Learning or NLP systems.\n\n- Strong programming skills in Python.\n\n- Hands-on experience with PyTorch and Hugging Face Transformers.\n\n- Experience working with Large Language Models (LLMs).\n\n- Strong understanding of Retrieval-Augmented Generation (RAG) architectures.\n\n- Experience with vector search technologies such as FAISS, Pinecone, Milvus, Chroma, or similar.\n\n- Experience with prompt engineering, LLM evaluation, and model fine-tuning.\n\n- Familiarity with LangChain, LangGraph, LlamaIndex, or similar orchestration frameworks.\n\n- Experience building REST APIs using FastAPI or similar frameworks.\n\n- Experience deploying ML solutions using Docker and cloud platforms (AWS, Azure, or GCP).\n\n- Good understanding of software engineering best practices, version control, testing, and CI/CD.\n\nPreferred Skills : \n\n- Experience with AI agents or multi-agent systems.\n\n- Experience with multilingual NLP applications.\n\n- Experience with recommendation systems or semantic search.\n\n- Experience with vLLM, Text Generation Inference (TGI), or LLM serving frameworks.\n\n- Familiarity with ML evaluation tools such as Arize Phoenix, LangSmith, or similar.\n\n- Experience working with enterprise AI applications in domains such as legal, healthcare, finance, or public sector.\n\n- Publications or open-source contributions in AI/ML are a plus.\n\nNice to Have : \n\n- Experience with distributed training, DeepSpeed, or FSDP.\n\n- Experience optimizing inference latency and deployment costs.\n\n- Knowledge of model monitoring, observability, and experimentation frameworks.\n\n- Familiarity with cloud-native architectures and microservices.\n\nWhat You'll Work On : \n\n- Production LLM applications\n\n- Retrieval-Augmented Generation (RAG)\n\n- AI Agents & Workflow Automation\n\n- Search & Recommendation Systems\n\n- Multilingual NLP\n\n- Evaluation & Observability\n\n- Document Intelligence\n\n- Prompt Engineering & Fine-tuning\n\n- Cloud-native ML Deployment\n\nIf you're passionate about building reliable AI systems that solve real-world problems at scale, we'd love to hear from you.\nSkills\nMachine Learning, Speech Recognition, Deep Learning, NLP, LLM, Python","description_format":"text","description_chars":3708,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Job Boards & Aggregators"],"lifecycle":[{"event":"open","at":"2026-09-25T18:04:06Z"}],"liveness":{"score":59,"band":"ok","label":"Likely open","p_open":0.85,"p_active":0.775,"p_room":0.9,"age_days":17,"expected_fill_days":28,"reasons":["seen:17","velocity","win:mid"],"computed_at":"2026-09-29T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/huntingcube-machine-learning-engineer","json_url":"https://alion.io/job/huntingcube-machine-learning-engineer.json","meta":{"generated_at":"2026-09-30T04:51:57Z","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":3439,"day_limit":5000,"remaining_today":1561,"minute_limit":60,"resets_at":"2026-10-01T00:00:00Z"}}}