{"id":1578462,"url":"https://alion.io/job/klimb-io-senior-ai-engineer","title":"Senior AI Engineer","company":{"id":1318,"name":"Klimb.io","domain":"klimb.io","url":"https://alion.io/company/klimb-io","size_band":"501-1000","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":"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":23000,"max_usd":48000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":27},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon 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AI","optional":false},{"name":"NLP","optional":false},{"name":"NLTK","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"PEFT","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"spaCy","optional":false},{"name":"SQL","optional":false},{"name":"Stable Diffusion","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transformers","optional":false},{"name":"Vertex AI","optional":false},{"name":"Weaviate","optional":false},{"name":"Weights & Biases","optional":false}],"status":"live","first_seen_at":"2026-10-01T11:43:20Z","employer_posted_date":null,"last_verified_at":"2026-10-01T11:43:20Z","board_verified":false,"closed_at":null,"days_open":12,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":12},"description":"Responsibilities:\nPartner with business, product, and engineering stakeholders to design and implement enterprise-scale AI solutions, with a strong emphasis on generative AI applications (LLMs, multimodal, and agentic AI).\nDefine and own the AI/ML roadmap for key problem areas, balancing near-term delivery with long-term innovation.\nLead design, prototyping, and deployment of generative AI models (GPT, Claude, LLaMA, Mistral, Stable Diffusion) for production use cases.\nBuild and optimize data pipelines, retrieval-augmented generation (RAG) systems, embedding strategies, and integrations with vector databases (FAISS, Pinecone, Weaviate, Milvus).\nEnsure robust model training, fine-tuning (LoRA, PEFT), orchestration (LangChain, LlamaIndex), monitoring, and governance.\nLead debugging and optimization of AI systems for latency, throughput, cost, and model drift/bias.\nCollaborate with ML engineers, data scientists, and MLOps teams to design scalable deployment pipelines using modern cloud and containerized environments.\nMentor and guide engineers, setting best practices for experimentation, evaluation, and production readiness.\nKeep abreast of the latest AI/ML research in LLMs, CV, NLP, and multimodal learning, driving adoption of cutting-edge methods.\nTranslate complex AI concepts into business outcomes for non-technical stakeholders.\nRequirements:\nExperience in AI/ML engineering, with at least 5+ years delivering generative AI models into production.\nBachelor's/Master's/PhD in Computer Science, Mathematics, Statistics, or a related field from a top-tier institution (IITs/NITs/BITs, etc. )\nStrong applied programming skills in Python, SQL, and R, and experience with data science libraries such as NumPy, Pandas, MATLAB, and scikit-learn.\nProven experience with deep learning frameworks: PyTorch, TensorFlow, Keras, MXNet, and Caffe.\nFamiliarity with NLP and ML libraries: Transformers, spaCy, Gensim, NLTK, and Hugging Face.\nExperience building and fine-tuning LLMs and integrating them with orchestration frameworks (LangChain, LlamaIndex).\nExpertise with vector databases (Pinecone, FAISS, Weaviate, Milvus) and knowledge of embedding retrieval patterns.\nCloud-native ML experience (AWS SageMaker, GCP Vertex AI, Azure ML) and containerization (Docker, Kubernetes).\nApplied knowledge of classical ML algorithms (SVM, decision trees, random forests, regression, and clustering) alongside modern DL/genAI approaches.\nStrong knowledge of CI/CD for ML, model observability (MLflow, Weights & Biases, LangSmith), and governance frameworks.\nExcellent problem-solving skills, communication, and ability to lead technical 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