{"id":3107480,"url":"https://alion.io/job/ollive-ai-ml-engineer","title":"AI / ML Engineer","company":{"id":799,"name":"Ollive","domain":"ollive.ai","url":"https://alion.io/company/ollive","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":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":13000,"max_usd":35000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":16},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Accelerate","optional":false},{"name":"AI Agents","optional":false},{"name":"DeepSpeed","optional":false},{"name":"Fine-tuning","optional":false},{"name":"FSDP","optional":false},{"name":"Hugging Face","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"PyTorch","optional":false},{"name":"QLoRA","optional":false},{"name":"SGLang","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"Transformers","optional":false},{"name":"vLLM","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"AWS","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Giskard","optional":true},{"name":"LLM","optional":true},{"name":"PEFT","optional":true},{"name":"Promptfoo","optional":true}],"status":"live","first_seen_at":"2026-10-22T19:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-22T19:35:00Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"We're looking for an engineer who has trained and fine-tuned models and knows how to build and operate the infrastructure required to do it reliably. You'll own the model layer behind our assessment engine by fine-tuning models for risk classification and adversarial evaluation, optimising them for production, and building the training and inference infrastructure that keeps our assessment pipeline fast, scalable, and cost-efficient. You'll work directly with the founders.\n\nResponsibilities: \nFine-tune open-weight models for risk assessment, adversarial/jailbreak detection, and AI agent evaluation.\nDesign and own the training infrastructure, including data pipelines (GT development), distributed training setup, experiment tracking, reproducibility, and operational workflows.\nOptimise models using quantisation, distillation, hyperparameter tuning, and inference optimisation while balancing latency, throughput, and cost.\nStand up and manage GPU/compute infrastructure (cloud and/or self-hosted), including cost modelling and capacity planning.\nDesign evaluation harnesses to measure model quality, robustness, and drift against the Ollive Risk Framework.\nDeploy, serve, and monitor models in production, including throttling, scaling, and failover handling.\nCollaborate with the Ollive AI Risk Labs and insurance team to translate risk methodologies into model behaviour.\n\nRequirements: \nStrong Machine Learning and Deep Learning fundamentals.\nHands-on experience fine-tuning LLMs/Foundation Models using LoRA, QLoRA, or full fine-tuning.\nStrong proficiency with PyTorch and the Hugging Face Transformers ecosystem.\nExperience with quantisation, hyperparameter tuning, and inference optimisation (including Speculative Decoding).\nExperience with distributed training frameworks such as DeepSpeed, FSDP, Accelerate, or similar.\nExperience setting up and operating training and serving infrastructure, including GPU provisioning, containerization, and model serving frameworks (SGLang, vLLM preferred).\n\nPreferred Skills: \nExperience training models from scratch or working with 2B+ parameter models.\nExperience with large-scale cloud inference platforms such as AWS Bedrock and Amazon SageMaker, including quota and throttling management.\n\nGood to Have: \nFamiliarity with AI security or adversarial ML tools such as Promptfoo, Garak, Giskard, or similar.\nExposure to regulated or compliance-heavy environments such as security, fintech, or insurance.\nSkills\nHugging Face, LLM, LoRA, ML, PyTorch, QLoRA, Transformers, deep learning, fine-tuning, foundation model, large language model, machine learning","description_format":"text","description_chars":2612,"description_truncated":false,"requirements":{"experience_years_min":2,"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":["Machine Learning","Security Compliance"],"lifecycle":[{"event":"open","at":"2026-10-22T19:35:00Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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