AI Solution Architect (Hands-On | Client-Facing | Full-Cycle Delivery)

Job Description

Techversant is looking for a visionary yet hands-on AI Solution Architect who thrives at the intersection of technology, business, and innovation. This is a client-facing leadership role for someone who can conceptualize, design, and deliver robust AI-driven applications while staying ahead of the curve with emerging AI trends and technologies. You will be responsible for the entire lifecycle of AI solution development from client discussions and requirements analysis to architecture design, algorithm selection, solution development, team guidance, and successful delivery. Key Responsibilities: Client Engagement & Consulting Interact directly with clients and stakeholders to understand complex business problems. Translate business needs into AI/ML opportunities and articulate solution strategies clearly. Present solution architectures, effort estimates, timelines, and potential ROI. Solution Design & Architecture Define end-to-end AI architecture including data pipelines, model selection, AI engines, integration layers, APIs, and deployment strategies. Make informed choices of AI/ML/DL algorithms, frameworks, and platforms based on problem type and constraints. Ensure the architecture is scalable, modular, secure, and optimized for performance. Hands-On Development Develop critical components or proof-of-concepts when needed. Perform hands-on design and coding for pipelines, model training, inferencing, or integrations. Create reusable components and automation scripts for repeatability. Team Leadership & Delivery Oversight Build and mentor a high-performing AI development team. Drive agile development cycles, ensuring timely and quality delivery. Collaborate with DevOps, Product Managers, and QA teams throughoutSDLC. Must-Have Skills: AI/ML/DL Expertise Proven experience with supervised, unsupervised, and reinforcement learning. Deep knowledge of ML algorithms (e.g., XGBoost, SVM, Random Forest) and DL architectures (CNNs, RNNs, Transformers, LLMs). Hands-on with Python, PyTorch, TensorFlow, Keras, Scikit-learn, Hugging Face, etc. Architecture & System Design Strong grasp on designing large-scale AI/ML pipelines using microservices, APIs, and cloud-native architecture. Experience designing MLOps workflows (CI/CD for ML) using tools like MLflow, Kubeflow, Airflow, DVC. Cloud & Platforms Proficient in AWS/GCP/Azure AI/ML services (e.g., SageMaker, Vertex AI, Azure ML Studio). Familiarity with vector databases (e.g., FAISS, Pinecone), model versioning, and deployment strategies (e.g., REST APIs, gRPC, containers, Kubernetes). Data Engineering Working knowledge of data wrangling, preprocessing, ETL, feature engineering, data versioning. Tools: Spark, Pandas, Snowflake, Kafka, Databricks, Delta Lake. Product & Project Ownership: Ability to estimate efforts, manage scope, and align delivery with client expectations. Experience in productizing AI models and integrating them into full-stack applications. Soft Skills Excellent communication, stakeholder management, and presentation skills. Ability to simplify complex AI concepts for non-technical stakeholders. Preferred/Nice-to-Have: Experience with Generative AI (LLMs, GPT, BERT, Stable Diffusion, LangChain, RAG, etc.) Knowledge of Prompt Engineering, Fine-Tuning, and RLHF Experience in NLP, Computer Vision, Time Series, or Recommendation Engines Understanding of ethical AI, explainable AI, and compliance in AI systems Publications, contributions to open-source, or AI certifications Education: Bachelors or Masters degree in Computer Science, Data Science, AI, or related fields. Certifications in Cloud AI/ML platforms or advanced AI specializations