{"id":2902651,"url":"https://alion.io/job/zocket-applied-ai-engineer","title":"Applied AI Engineer","company":{"id":3045572,"name":"Zocket","domain":"zocket.com","url":"https://alion.io/company/zocket-com","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":44000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":38},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Agentic Workflows","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Feature Store","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Model Distillation","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"TensorFlow","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false},{"name":"DPO","optional":true},{"name":"GitHub","optional":true},{"name":"LLM Evaluation","optional":true},{"name":"LoRA","optional":true},{"name":"Multimodal AI","optional":true},{"name":"PEFT","optional":true},{"name":"Post-training","optional":true},{"name":"QLoRA","optional":true},{"name":"RLHF","optional":true}],"status":"live","first_seen_at":"2026-09-03T08:46:07Z","employer_posted_date":"2026-09-03","last_verified_at":"2026-10-21T23:13:30Z","board_verified":true,"closed_at":null,"days_open":48,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":48},"description":"About the role\nWe are looking for a Senior AI/ML Engineer who can take AI features from prototype to production with\nconfidence. You will own the full lifecycle of our LLM-powered systems - from benchmarking model and\npipeline performance, to hardening the stack for scale, to shipping it live to real users. This role sits at the\nintersection of applied LLM/GenAI work and MLOps, and is critical to how quickly and reliably we can put new AI\ncapabilities in front of customers.\nYou will work closely with product, backend, and design to make sure what we ship is fast, accurate, cost-\nefficient, and observable in production.\nWhat you will do\nDesign, build, and ship LLM-powered features end-to-end - including RAG pipelines, agentic workflows,\nprompt orchestration, and fine-tuning where it makes sense.\nDefine and run benchmarking frameworks for our AI applications: latency, throughput, accuracy,\nhallucination rate, cost per request, and quality regressions across model and prompt changes.\nEstablish offline evals (golden sets, LLM-as-judge, human-in-the-loop) and online evals (A/B tests,\nshadow traffic, canary releases) before any model or prompt goes live.\nTake models and pipelines to production: containerize, deploy, autoscale, and monitor inference\nservices with clear SLOs for latency, error rate, and cost.\nBuild the MLOps backbone - CI/CD for models and prompts, versioning, feature stores where needed,\nobservability (traces, metrics, logs), and rollback paths.\nOptimize inference performance and cost: batching, caching, quantization, distillation, model routing,\nand choosing the right managed vs. self-hosted trade-offs.\nPartner with product to translate fuzzy product asks into measurable AI quality bars, and own the “is\nthis good enough to ship?” decision with data behind it.\nMentor other engineers on LLM best practices, eval rigor, and production readiness.\nWhat we are looking for\n3-6 years of engineering experience, with a meaningful portion spent shipping ML or AI systems to\nproduction (not just notebooks or POCs).\nStrong hands-on experience with LLMs and GenAI: at least one production system using OpenAI /\nAnthropic / open-source models, plus practical experience with RAG, embeddings, vector stores, and\nprompt engineering.\nSolid MLOps foundation - model serving (FastAPI, vLLM, Triton, SageMaker, or similar),\ncontainerization (Docker, Kubernetes), and at least one cloud (AWS, GCP, or Azure).\nDemonstrated ability to benchmark systems rigorously: you can talk concretely about how you\nmeasured a model’s quality and performance, what you optimized, and what you knowingly traded off.\nStrong Python skills; comfortable with PyTorch or TensorFlow, and with frameworks like LangChain,\nLlamaIndex, or equivalent (or a clear point of view on why not to use them).\nGood engineering discipline: testing, code review, clear API design, and the instinct to add observability\nbefore it is needed.\nComfortable owning the path to production - you have taken something live, watched it break, and\nfixed it, and you do not need a separate team to do that for you.\nBonus points\nExperience fine-tuning or post-training open-source models (LoRA/QLoRA, DPO, RLHF).\nWorked with multimodal models (image, video, or audio generation/understanding).\nBuilt or contributed to an internal eval harness or LLM observability tooling.\nExperience with high-QPS, low-latency inference at consumer scale.\nOpen-source contributions or technical writing in the AI/ML space.\nWhat success looks like in your first 6 months\nYou have shipped at least one customer-facing AI feature to production, fully owned by you.\nA benchmarking and evals framework is in place, run on every model or prompt change, with results\nvisible to the team.\nProduction AI services have clear SLOs, dashboards, and alerting - and you can answer “what is this\ncosting us per 1,000 requests?” without thinking.\nThe team’s velocity on shipping AI features has measurably increased because of the infrastructure and\npatterns you put in place.\nWhy Zocket\nZocket is building the AI layer for marketing - letting any business create high-performing ads, creatives, and\ncampaigns in minutes instead of weeks. AI is not a side project here; it is the product. You will work on systems\nthat thousands of businesses use every day, with a tight team, fast feedback loops, and a real mandate to ship.\nHow to apply\nSend your resume and links to anything you have shipped (GitHub, projects, papers, demos) to\n. If you have taken an AI system from prototype to production and have a story about how\nyou knew it was ready, lead with that.","description_format":"text","description_chars":4618,"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":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Advertising"],"lifecycle":[{"event":"open","at":"2026-10-20T06:58:27Z"}],"visa":[],"liveness":{"score":25,"band":"fade","label":"Fading","p_open":1,"p_active":0.559,"p_room":0.45,"age_days":47,"expected_fill_days":27,"reasons":["conf:1","win:tail"],"computed_at":"2026-10-21T05:46:15Z"},"pay":null,"html_url":"https://alion.io/job/zocket-applied-ai-engineer","json_url":"https://alion.io/job/zocket-applied-ai-engineer.json","meta":{"generated_at":"2026-10-21T23:16:17Z","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":33099,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-22T00:00:00Z"}},"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":3045572},"rest":"https://alion.io/mcp/rest/get_company?id=3045572"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fzocket-applied-ai-engineer"},{"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%2Fzocket-applied-ai-engineer"},{"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%2Fzocket-applied-ai-engineer"},{"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/zocket-applied-ai-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fzocket-applied-ai-engineer"}]}