{"id":3798340,"url":"https://alion.io/job/symphoni-hr-aiml-engineer-reinforcement-learning","title":"AI/ML Engineer - Reinforcement Learning","company":{"id":3800108,"name":"Symphoni HR","domain":"symphonihr.com","url":"https://alion.io/company/symphoni-hr","size_band":"201-500","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":{"grade":"A","score":93,"open_postings":42,"ghost_share":0,"stale_share":0,"repost_share":0.476,"time_to_fill_p50_days":45,"computed_at":"2026-11-09T06:03:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","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":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"PPO","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Ray","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"RLlib","optional":false}],"status":"live","first_seen_at":"2026-11-05T10:23:58Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-09T06:55:28Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"This role focuses on designing, training and deploying reinforcement learning models that solve complex decision-making problems. The successful candidate will work on policy optimization, reward design and neural network architectures, taking models from research prototypes to production-grade systems. You will collaborate with data scientists, engineers and domain experts to deliver measurable business impact.\nA leading global professional services and finance organization with a large footprint across accounting, assurance, advisory and financial consulting. The firm serves clients across many industries and invests heavily in data, analytics and artificial intelligence to transform how businesses operate. Its culture emphasizes innovation, continuous learning and collaborative problem-solving across diverse teams.\nResponsibilities\nDesign, build and train reinforcement learning models for complex sequential decision-making problems.\nImplement and evaluate algorithms such as PPO, DQN, Actor Critic and SAC using PyTorch and Ray RLlib.\nDefine Markov Decision Processes and develop effective reward shaping strategies.\nDevelop and optimize neural network architectures, including MLP and RNN models, for policy and value learning.\nRun experiments, tune hyperparameters and benchmark model performance against defined business metrics.\nTranslate business requirements into scalable ML solutions and communicate results to technical and non-technical stakeholders.\nDeploy, monitor and maintain models in production environments in collaboration with engineering teams.\nStay current with advances in reinforcement learning and AI research and apply relevant innovations.\nRequirements\n7 to 12 years of experience in machine learning, data science or AI engineering.\nStrong hands-on experience in reinforcement learning, including policy optimization and reward shaping.\nProficiency in Python and deep learning frameworks such as PyTorch.\nPractical experience with Ray RLlib or similar reinforcement learning libraries.\nSolid understanding of neural networks (MLP, RNN), Markov Decision Processes and actor-critic methods.\nExperience training, evaluating and deploying machine learning models at scale.\nStrong analytical, problem-solving and communication skills.\nAbility to work in a hybrid arrangement based in Bengaluru.\nSkills\nReinforcement Learning, Training models, Python, PyTorch, Data Science, Machine Learning, AI, Ray RLlib, Neural Networks (MLP, RNN), PPO, DQN, Actor Critic, SAC, MDP, Reward Shaping, Policy 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