{"id":1469124,"url":"https://alion.io/job/accelya-specialist-aiml-software-development","title":"Specialist - AIML Software Development","company":{"id":2893274,"name":"Accelya","domain":"accelya.com","url":"https://alion.io/company/accelya","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19500,"max_usd":48000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":22},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon EC2","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Feature Store","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Gemini","optional":false},{"name":"IAM","optional":false},{"name":"Keras","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Kubeflow","optional":false},{"name":"Llama","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Mistral","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multimodal AI","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"QLoRA","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Self-Supervised Learning","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transfer Learning","optional":false},{"name":"Transformers","optional":false},{"name":"Weights & Biases","optional":false},{"name":"AWS CDK","optional":true},{"name":"DeepSpeed","optional":true},{"name":"FAISS","optional":true},{"name":"FSDP","optional":true},{"name":"Kubernetes","optional":true},{"name":"PEFT","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Pulumi","optional":true},{"name":"Ray","optional":true},{"name":"Recommender Systems","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"Terraform","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-09-22T00:00:00Z","employer_posted_date":"2026-09-22","last_verified_at":"2026-09-29T15:37:23Z","board_verified":true,"closed_at":null,"days_open":8,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":8},"description":"For more than 40 years, Accelya has been the industry’s partner for change, simplifying airline financial and commercial processes and empowering the air transport community to take better control of the future. Whether partnering with IATA on industry-wide initiatives or enabling digital transformation to simplify airline processes, Accelya drives the airline industry forward and proudly puts control back in the hands of airlines so they can move further, faster.\nRoles & Responsibilities\nAI/ML Strategy & Architecture\nDefine and own end-to-end AI/ML architectures for business-critical products and platforms.\nLead technical evaluations and design decisions for machine learning, deep learning, and generative AI initiatives.\nEstablish engineering standards, model development practices, and deployment frameworks for AI systems.\nDrive architecture reviews and provide technical guidance on model selection, feature engineering, infrastructure, and deployment strategies.\nProduce technical design documents, architecture decision records (ADRs), and solution blueprints for AI initiatives.\nEvaluate emerging AI technologies and determine their applicability to business problems.\nMachine Learning & Predictive Modeling\nArchitect and oversee production machine learning systems across classification, regression, forecasting, recommendation, optimization, and anomaly detection use cases.\nLead advanced feature engineering, model selection, hyperparameter optimization, and model evaluation initiatives.\nDesign robust experimentation frameworks for evaluating model performance and business impact.\nEstablish standards for training, validation, testing, and deployment of machine learning models.\nAddress challenges related to model drift, bias, explainability, fairness, and performance degradation in production.\nDrive adoption of best practices in statistical modeling and predictive analytics.\nDeep Learning & Applied AI\nDesign and productionize deep learning solutions using CNNs, RNNs, LSTMs, Transformers, and modern neural architectures.\nLead initiatives involving transfer learning, representation learning, self-supervised learning, and multimodal AI systems.\nOptimize model performance through pruning, quantization, distillation, and inference acceleration techniques.\nEvaluate and integrate state-of-the-art research advancements into production AI systems.\nBuild scalable training and inference pipelines for large-scale deep learning workloads.\nGenerative AI & Large Language Models\nDesign and implement production-ready LLM-powered solutions including Retrieval-Augmented Generation (RAG) systems.\nDevelop robust embedding, retrieval, ranking, and knowledge-grounding strategies.\nLead model evaluation efforts focused on factuality, safety, latency, reliability, and cost optimization.\nWork with proprietary and open-source foundation models including OpenAI, Anthropic, Gemini, Llama, and Mistral ecosystems.\nImplement fine-tuning and adaptation techniques such as LoRA, QLoRA, and instruction tuning where appropriate.\nCollaborate with product teams to identify opportunities for integrating generative AI capabilities into business workflows.\nMLOps, Infrastructure & Production Engineering\nDefine and drive MLOps strategy including model registries, experiment tracking, feature stores, and deployment pipelines.\nDesign CI/CD processes for AI/ML workloads covering validation, testing, rollout, monitoring, and rollback mechanisms.\nArchitect cloud-native AI infrastructure on AWS leveraging SageMaker, EC2, Lambda, EKS/ECS, S3, and CloudWatch.\nEnsure reproducibility and scalability of AI workloads through Docker, Infrastructure-as-Code, and modern deployment practices.\nImplement monitoring frameworks covering data quality, model drift, prediction performance, latency, availability, and system reliability.\nEstablish operational SLAs and governance standards for production AI systems.\nRequired Skills\nCore - Must Have\n3+ years of experience building and operating production AI/ML systems.\nExpert-level Python proficiency with strong software engineering fundamentals.\nDeep understanding of machine learning algorithms, statistical modeling, model evaluation, and optimization techniques.\nExtensive experience with machine learning frameworks such as Scikit-learn, PyTorch, TensorFlow, or Keras.\nStrong expertise in deep learning architectures including CNNs, RNNs, LSTMs, Attention Mechanisms, and Transformers.\nHands-on experience deploying machine learning solutions in production environments.\nExperience with Generative AI and Large Language Model applications including RAG architectures and embedding-based systems.\nStrong AWS experience across SageMaker, EC2, S3, Lambda, IAM, CloudWatch, and related services.\nAdvanced Docker proficiency and containerized deployment experience.\nPractical experience with CI/CD pipelines and software delivery practices.\nExperience with MLOps platforms such as MLflow, Weights & Biases, Kubeflow, or equivalent.\nStrong understanding of experimentation frameworks, A/B testing methodologies, and model performance measurement.\nExcellent problem-solving, debugging, and technical decision-making skills.\nGood to Have\nExperience with distributed training technologies such as Ray, DeepSpeed, or PyTorch FSDP.\nKnowledge of vector databases including Pinecone, Weaviate, FAISS, ChromaDB, or pgvector.\nExperience with reinforcement learning, causal inference, or Bayesian modeling.\nFamiliarity with Terraform, AWS CDK, Pulumi, or Infrastructure-as-Code tools.\nExposure to multimodal AI systems involving text, image, and structured data.\nExperience with recommendation systems, ranking models, search relevance, or personalization engines.\nContributions to open-source AI projects, technical publications, or research initiatives.\nStartup or product-company experience operating in fast-moving environments.\nEducation\nMaster's Degree (preferred) or Bachelor's Degree in one of the following disciplines, or equivalent professional experience:\nComputer Science\nArtificial Intelligence\nMachine Learning\nData Science\nStatistics\nMathematics\nComputer Engineering\nRelated quantitative or computational fields\nRelevant certifications such as:\nAWS Certified Machine Learning Specialty\nAWS Solutions Architect\nGoogle Professional Machine Learning Engineer\nDeepLearning.AI Specializations\nare valued but are not substitutes for demonstrated engineering capability.\nExperience\n4+ years of progressive hands-on experience in AI/ML Engineering, Machine Learning, Data Science, or Applied AI, with evidence of increasing technical ownership and leadership.\nWe are looking for evidence of:\nEnd-to-end ownership of production AI/ML systems from problem definition through deployment and continuous improvement.\nStrong experience applying machine learning and deep learning techniques to solve real-world business problems.\nExperience building and maintaining production-grade AI platforms and services.\nDemonstrated technical leadership through architecture ownership, design reviews, mentoring, and engineering excellence initiatives.\nExperience collaborating with product, engineering, and business stakeholders to define and execute AI strategies.\nAbility to navigate technical ambiguity and convert complex business challenges into scalable AI solutions.\nWhat does the future of the air transport industry look like to you? Whether you’re an industry veteran or someone with experience from other industries, we want to make your ambitions a reality!","description_format":"text","description_chars":7503,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Professional Services","Travel & Tourism","Commercial Aviation"],"lifecycle":[{"event":"open","at":"2026-09-29T15:37:23Z"}],"liveness":{"score":77,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.768,"p_room":1,"age_days":8,"expected_fill_days":24,"reasons":["conf:14","win:early"],"computed_at":"2026-09-30T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/accelya-specialist-aiml-software-development","json_url":"https://alion.io/job/accelya-specialist-aiml-software-development.json","meta":{"generated_at":"2026-09-30T05:58:35Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":4185,"day_limit":5000,"remaining_today":815,"minute_limit":60,"resets_at":"2026-10-01T00:00:00Z"}}}