{"id":2847110,"url":"https://alion.io/job/hackertrail-applied-ai-engineer-2","title":"Applied AI Engineer","company":{"id":2328067,"name":"HackerTrail","domain":"hackertrail.com","url":"https://alion.io/company/hackertrail","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","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":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Fine-tuning","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Pre-training","optional":false},{"name":"Python","optional":false},{"name":"QLoRA","optional":false},{"name":"RAG","optional":false},{"name":"SFT","optional":false},{"name":"Amazon ECS","optional":true},{"name":"Amazon EKS","optional":true},{"name":"AWS","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"AWS Lambda","optional":true},{"name":"Docker","optional":true},{"name":"FAISS","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"Milvus","optional":true},{"name":"OpenSearch","optional":true},{"name":"PEFT","optional":true},{"name":"Pinecone","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-10-19T09:27:25Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-25T11:28:40Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"About the role\nLLM Engineer (GenAI) We are seeking a Deep Learning Architect with 3-4 years of experience to help design and build production-grade GenAI systems. In this role, you will contribute architecture coverage across the team-reviewing system designs, identifying gaps, and guiding technical decisions at the solution level. You will work on end-to-end LLM system design, Retrieval-Augmented Generation (RAG) pipelines, and multi-agent architectures, with a strong focus on production readiness. Strong coding depth is non-negotiable. What You'll Do & The Impact You'll Have Design and contribute to end-to-end LLM system architecture for real-world enterprise use cases (from requirements to production). Pre-train, fine-tune LLMs and domain-specific models using techniques such as CPT, SFT, LoRA, and QLoRA for client-specific use cases. Design and run model evaluation pipelines to benchmark performance, accuracy, and cost across different fine-tuning approaches. Optimise models for latency, throughput, token efficiency, and inference cost in production environments. Implement prompt versioning, rollback strategies, and model monitoring to ensure reliability post-deployment. Collaborate with cross-functional stakeholders (engineering, data, product, client teams) to deliver high-impact solutions on tight timelines.\nWhat you'll bring\nWhat You'll Bring to the Table Must-Have Skills 3-6 years of experience in ML engineering, LLMs, or model development roles. Hands-on experience with Continual pre-training (CPT), supervised fine-tuning (SFT), LoRA, or QLoRA on LLMs. Strong Python programming skills. Ability to write clean, testable, production-ready code. Experience running model evaluation and benchmarking pipelines in a structured way. Solid understanding of transformer architectures and how fine-tuning affects model behaviour. Experience deploying fine-tuned and pre-trained models to cloud environments with attention to cost and latency. Nice-to-Have Skills Experience with AWS-native GenAI building blocks (e.g., Bedrock, OpenSearch, Lambda, ECS/EKS) and secure enterprise deployments. Experience with vector databases/search engines (OpenSearch, Pinecone, Weaviate, Milvus, FAISS) and retrieval optimization. Experience with containerization and orchestration (Docker, Kubernetes). Experience building evaluation/observability pipelines for LLM systems and implementing safety/guardrail patterns. Consulting or client-facing delivery experience.\nBenefits and perks\nWhat We Bring to the Table Impact-first culture where your work ships to production and drives measurable business outcomes. Professional growth through mentorship, continuous learning, and exposure to diverse enterprise environments. A high-performing team that values curiosity, integrity, ownership, and engineering excellence. Opportunities for regional and international exposure as projects require.","description_format":"text","description_chars":2905,"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":["Continuous learning"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-19T11:34:59Z"}],"visa":[],"liveness":{"score":54,"band":"ok","label":"Likely open","p_open":1,"p_active":0.537,"p_room":1,"age_days":5,"expected_fill_days":30,"reasons":["conf:3","agency","velocity","win:early"],"computed_at":"2026-10-25T05:49:26Z"},"pay":null,"html_url":"https://alion.io/job/hackertrail-applied-ai-engineer-2","json_url":"https://alion.io/job/hackertrail-applied-ai-engineer-2.json","meta":{"generated_at":"2026-10-25T13:50:29Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":19800,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-26T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":2328067},"rest":"https://alion.io/mcp/rest/get_company?id=2328067"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fhackertrail-applied-ai-engineer-2"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fhackertrail-applied-ai-engineer-2"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fhackertrail-applied-ai-engineer-2"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/hackertrail-applied-ai-engineer-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fhackertrail-applied-ai-engineer-2"}]}