{"id":3276183,"url":"https://alion.io/job/idfc-first-bank-ml-engineer","title":"ML Engineer","company":{"id":720227,"name":"IDFC FIRST Bank","domain":"idfcfirst.bank.in","url":"https://alion.io/company/idfcfirst","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":"middle","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":21000,"max_usd":51000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":32},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"BERT","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"GPT-4","optional":false},{"name":"Hadoop","optional":false},{"name":"Keras","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Spark","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transformers","optional":false},{"name":"Kubeflow","optional":true},{"name":"MLFlow","optional":true}],"status":"live","first_seen_at":"2026-10-26T14:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-26T14:35:00Z","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 someone who's as comfortable building ML pipelines as they are optimizing infrastructure for scale. If you thrive on solving real-world data challenges, love experimenting, and don't shy away from getting your hands dirty with deployment, this is your jam.\n\nResponsibilities: \nApply a strong understanding of machine learning principles and algorithms, with a focus on LLMs such as GPT-4 BERT, and similar architectures.\nLeveraged deep learning frameworks like TensorFlow, PyTorch, or Keras to train and fine-tune LLMs.\nUtilize deep knowledge of computer architecture, especially GPUs, to maximize utilization and efficiency.\nWork with cloud platforms(AWS, Azure, GCP) to manage and optimize resources for training large-scale deep learning models.\nUse containerization and orchestration tools(Docker, Kubernetes) for scalable and reproducible ML deployments.\nApply principles of parallel and distributed computing, including distributed training for deep learning models.\nWork with big data and distributed computing technologies(Hadoop, Spark) to handle large-volume datasets.\nImplement MLOps practices and use related tools to manage the complete ML lifecycle.\nContribute to the infrastructure side of multiple ML projects, particularly those involving deep learning models such as BERT and Transformers.\nManage resources and optimize performance for large-scale ML workloads, both on-premise and in the cloud.\nHandle challenges in training large models, including memory management, optimizing data loading, and troubleshooting hardware issues.\nCollaborate closely with data scientists and ML engineers to understand infrastructure needs and deliver efficient solutions.\n\nRequirements: \nStrong knowledge of machine learning and deep learning algorithms, especially LLMs.\nProficiency in Python and deep learning frameworks (TensorFlow, PyTorch, Keras).\nExpertise in GPU architecture and optimization.\nExperience with parallel and distributed computing concepts.\nHands-on with containerization (Docker) and orchestration (Kubernetes).\n\nTech Stack and Tools: \nCloud: AWS, Azure, GCP.\nBig Data: Hadoop, Spark.\nMLOps Tools: MLflow, Kubeflow, or similar.\nInfrastructure Optimization: Resource allocation, distributed training, GPU performance tuning.\n\nNice-to-Have: \nPrior experience training large-scale deep learning models(BERT, Transformers).\nExposure to high-scale environments and large datasets.\nAbility to troubleshoot hardware bottlenecks and optimize data pipelines.\nSkills\nDocker, Keras, Kubernetes, ML, PyTorch, TensorFlow, machine learning","description_format":"text","description_chars":2568,"description_truncated":false,"requirements":{"experience_years_min":4,"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":["Commercial & Retail Banks","Cards & Card 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