{"id":1280256,"url":"https://alion.io/job/enter-senior-machine-learning-engineer","title":"Senior Machine Learning Engineer","company":{"id":3800358,"name":"Enter Recruitment","domain":"enterrecruitment.com","url":"https://alion.io/company/enter-10","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","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":24000,"max_usd":52000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon EKS","optional":false},{"name":"Anthropic","optional":false},{"name":"Azure AKS","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Flask","optional":false},{"name":"Google GKE","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"OCR","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Speech Recognition","optional":false},{"name":"Text-to-Speech","optional":false},{"name":"Apache Kafka","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Dagster","optional":true},{"name":"FAISS","optional":true},{"name":"GCP","optional":true},{"name":"Pinecone","optional":true},{"name":"Prefect","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-08-20T04:15:19Z","employer_posted_date":null,"last_verified_at":"2026-08-20T04:15:19Z","board_verified":false,"closed_at":null,"days_open":57,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":57},"description":"Job Description :\n\nWe're looking for a Senior Machine Learning Engineer (3 to 6 years) to lead the design, development, and deployment of AI-powered systems. This role combines hands-on ML engineering, backend development, LLM integration, and production-grade infrastructure.\n\nThe candidate will have responsibilities across the following functions :\n\nMachine Learning and LLMs :\n\n- Integrate LLMs (OpenAI, Anthropic, etc.) into pipelines; prompting, workflows, RAG, evaluation, and iteration.\n\n- Architect and develop Voice AI systems using technologies such as ASR, TTS, LLMs, and conversational AI.\n\n- Design robust prompt engineering strategies and maintain prompt libraries across environments.\n\n- Build AI systems for OCR, document understanding, information extraction, and classification.\n\n- Improve model performance via fine-tuning, quantisation, pruning, or distillation when needed.\n\n- Build, train, fine-tune, and optimise ML and LLM-based models for production use cases.\n\nBackend Engineering :\n\n- Develop scalable backend systems using Python (FastAPI/Flask preferred).\n\n- Architect and integrate REST APIs, rate limiting, and monitoring.\n\n- Debug, profile, and optimise API performance in production.\n\nInfrastructure and DevOps :\n\n- Build and deploy containerised applications using Docker.\n\n- Manage model and service deployments on Kubernetes (EKS, GKE, AKS or self-managed clusters).\n\n- Work with CI/CD pipelines to ensure smooth releases and automated testing.\n\n- Implement logging, monitoring, and alerting for ML and backend services.\n\nCollaboration and Leadership :\n\n- Work closely with cross-functional teams to convert business problems into ML solutions.\n\n- Provide technical guidance to junior engineers and contribute to architectural decisions.\n\n- Bring a strong bias for shipping, iteration, and maintaining high engineering standards.\n\nRequirements :\n\n- 3 to 6 years of hands-on experience as an ML Engineer or similar role.\n\n- Expert-level Python programming and clean code practices.\n\n- Strong experience designing and integrating production APIs.\n\n- Practical experience integrating LLM models and writing optimised prompts.\n\n- Strong understanding of model fine-tuning, hyperparameter tuning, and inference optimisation.\n\n- Experience with Docker, containerised deployments, and Kubernetes orchestration.\n\n- Good understanding of microservices architecture, distributed systems, and cloud infrastructure.\n\n- Solid problem-solving and debugging skills across the ML lifecycle.\n\nNice-to-Have :\n\n- Experience with vector databases (Pinecone, Weaviate, FAISS).\n\n- Experience with event-driven architecture (Kafka, Pub/Sub, SQS/SNS).\n\n- Exposure to data pipelines (Airflow, Prefect, Dagster)\nSkills\nMachine Learning, LLM, Python, RAG, Prompt Engineering, FastAPI, Conversational AI, 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