3 days ago
Bengaluru, IndiaMid Level / Senior
Responsibilities
- Translate business and user needs into data requirements, ML/AI problem statements, experiments, success metrics, designs, and technical tasks.
- Perform data exploration, feature engineering, model selection, training, tuning, evaluation, explainability, error analysis, and champion-challenger comparisons.
- Build predictive, forecasting, recommendation, optimization, NLP, computer vision, and other machine learning solutions.
- Develop foundation-model applications using prompt engineering, embeddings, vector search, Retrieval-Augmented Generation, structured outputs, evaluation, and guardrails.
- Implement agentic workflows involving tool and function calling, planning, memory and context, orchestration, multi-agent patterns, and human-in-the-loop controls.
- Build production-quality Python components, APIs, services, containers, enterprise integrations, and batch or real-time inference workflows.
- Implement experiment tracking, data and model versioning, prompt or configuration versioning, registries, CI/CD, continuous training, promotion, rollback, and retraining pipelines.
- Configure evaluation, monitoring, and observability for model quality, drift, robustness, safety, latency, reliability, cost, adoption, and business KPIs.
- Apply responsible AI, security, privacy, testing, reproducibility, auditability, and human oversight requirements.
- Collaborate with data engineers, software and cloud engineers, architects, UX teams, domain specialists, and product owners.
- Own a technical module or workstream, review code and model artifacts, coordinate Analysts or Senior Analysts, manage dependencies and risks, and mentor junior team members.
- Participate in client workshops, design walkthroughs, demonstrations, technical documentation, and reusable accelerator development.
Requirements
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related discipline.
- 4–8 years of progressive experience in machine learning, AI engineering, data science, and/or software engineering, including ownership of production AI workstreams.
- Strong hands-on Python and SQL skills with production-quality software engineering, version control, testing, modular design, and API or service development.
- Strong foundations in supervised and unsupervised learning, statistics, feature engineering, model evaluation, experimentation, and technique selection.
- Hands-on experience with ML and deep learning libraries such as scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, or equivalent.
- Experience taking at least one ML/AI solution from experimentation through production deployment, monitoring, and ongoing support.
- Hands-on generative AI experience with prompt engineering, embeddings, vector search, Retrieval-Augmented Generation, structured outputs, evaluation, and guardrails.
- Experience with agentic AI patterns and frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, or equivalent.
- Experience with MLOps, LLMOps, and/or AgentOps practices, including tracking, versioning, registries, CI/CD, rollback, retraining, and lifecycle monitoring.
- Experience with at least one major cloud or AI platform such as AWS, Azure, GCP, Databricks, Snowflake, or Anthropic and its relevant AI services.
- Experience deploying AI through APIs, containers, Kubernetes, or managed services, including batch or real-time inference; familiarity with FastAPI or Docker.
- Working knowledge of data platforms and pipelines, relational and vector databases, unstructured data, and API-based integrations.
- Experience monitoring accuracy, drift, robustness, safety, latency, reliability, cost, adoption, and business outcomes.
- Awareness of responsible AI, security, privacy, observability, reproducibility, auditability, testing, and human oversight for enterprise production.
- Strong analytical, problem-solving, stakeholder engagement, and communication skills.
- Experience leading a technical module or small workstream, estimating tasks, reviewing code and model artifacts, managing dependencies, and mentoring junior team members.
- Preferred qualifications include a Master's degree, PhD, recognized cloud or AI certifications, consulting experience, advanced expertise in areas such as time series or recommender systems, Kubernetes or model serving experience, GPU or NVIDIA experience, AI governance or red-teaming experience, and relevant industry or client-facing domain experience.
Benefits
- Worldwide network of data scientists, ML/AI engineers, and lifecycle engineering practitioners.
- World-class training, mentorship, and certifications across machine learning, deep learning, Generative AI, Agentic AI, MLOps, LLMOps, AgentOps, and cloud AI platforms.
- Opportunity to work on high-visibility client engagements and contribute to reusable model, agent, evaluation, and engineering assets.
Tech Stack
AWSAzureDatabricksDockerFastAPIGoogle Cloud PlatformKubernetesLightGBMPythonPyTorchscikit-learnSnowflakeSQLTensorFlowXGBoost
Categories
About Accenture
Accenture is a global professional services firm providing management consulting, systems integration and technology, cybersecurity, and business process outsourcing for enterprises and governments. It operates a services-driven model delivering projects and managed services, often with major cloud and software partners, across industries worldwide. Headquartered in Dublin and publicly traded on the NYSE (ACN), it originated as Andersen Consulting and adopted the Accenture name in 2001.