{"id":1900164,"url":"https://alion.io/job/infosys-aimlgen-ai-engineer","title":"AI/ML/GEN AI Engineer","company":{"id":223,"name":"Infosys","domain":"infosys.com","url":"https://alion.io/company/infosys","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"A","score":95,"open_postings":249,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":93,"computed_at":"2026-10-12T06:55:09Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"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":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Copilot Studio","optional":false},{"name":"GCP","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"Tokenization","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"Angular","optional":true},{"name":"Apache Kafka","optional":true},{"name":"Django","optional":true},{"name":"Explainable AI","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Flask","optional":true},{"name":"JavaScript","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM","optional":true},{"name":"LoRA","optional":true},{"name":"Model Distillation","optional":true},{"name":"PEFT","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"QLoRA","optional":true},{"name":"Quantization","optional":true},{"name":"React.js","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-10-05T05:26:28Z","employer_posted_date":"2026-10-05","last_verified_at":"2026-10-12T15:48:28Z","board_verified":true,"closed_at":null,"days_open":7,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":7},"description":"We are seeking a dynamic candidate with expertise in Python, Machine Learning, Natural Language Processing (NLP) & GenAI techniques. The ideal candidate should have hands-on experience in designing and implementing end-to-end data science and ML solutions, including model productionisation and guiding development teams on ML use case implementation. A strong background in AI/ML solutioning combined with experience in NLP & GenAI solutions is highly preferred. Candidates with experience in Microsoft Copilot Studio will be a great asset to the team.\nResponsibilities\nPerform data collection, profiling, exploration data analysis (EDA), and data preparation.\nApply a range of ML techniques including supervised, unsupervised, and reinforcement learning.\nDesign, develop, and deploy machine learning models using Python and popular ML frameworks\nImplement NLP solutions using NLP techniques like preprocessing, tokenization, vectorization, and semantic analysis.\nDevelop and deploy GenAI solutions such as RAG systems and Agentic AI.\nMonitor model performance in production and implement retraining strategies.\nAdhere to and implement Responsible AI principles in all ML workflows.\nPresent analytical insights to business stakeholders and project teams.\nPropose ML-based solutions and provide effort estimates for new use cases.\nCollaborate with data scientists and engineers on model training, evaluation, and deployment.\nUtilize AI services from cloud platforms such as Azure, AWS, and GCP.\nTechnical requirements\nStrong proficiency in Python for data processing, automation, and model development.\nDeep understanding of ML model lifecycle: training, evaluation, and deployment.\nStrong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).\nGood to have experience integrating GenAI capabilities into enterprise applications using platforms like Microsoft Copilot Studio.\nGood to have experience in monitoring model performance and conduct thorough evaluations using metrics such as Precision, Recall, F1 Score, and BLEU\nUnderstanding of Responsible AI practices including model fairness, transparency, and auditability.\nHands-on experience with Python-based web applications for AI/ML use cases.\nSolid knowledge of cloud-based AI services (Azure, AWS, GCP).\nAdditional responsibilities\nExperience with MLOps frameworks for model lifecycle, versioning, deployment, and monitoring - such as Azure Machine Learning or AWS Sagemaker.\nExperience with Python-based web frameworks such as Flask and Django is essential, and familiarity with front-end technologies like Angular or React.js is a valuable addition.\nHands-on experience in fine-tuning large language models (LLM) using techniques such as LoRA and QLoRA is highly valued.\nExperience with Kubernetes, docker containerization, and Kafka is preferred. Knowledge on model optimization, model distillation, quantization is an advantage.\nPreferred skills\nTechnology->AI-AI Engineering->AI/ML Solution Architecture and Design->generative ai,Technology->AI-Generative AI->Conversational AI Platform,Technology->AI-Generative AI->Prompt Engineering,Technology->AI-Responsible AI->Responsible AI->explainable ai\nEducation\nBachelor Of Comp. 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