AI Solutions Architect

AI Solutions Engineer

About Us

Automation Anywhere is the leader in Agentic Process Automation (APA), transforming how work gets done with AI-powered automation. Its APA system, built on the industry's first Process Reasoning Engine (PRE) and specialized AI agents, combines process discovery, RPA, end-to-end orchestration, document processing, and analytics—all delivered with enterprise-grade security and governance. Guided by its vision to fuel the future of work, Automation Anywhere helps organizations worldwide boost productivity, accelerate growth, and unleash human potential.

Key Activities

• Design, build, and maintain scalable ETL/ELT pipelines for batch and real-time data processing.

• Collect and integrate data from various structured (APIs, databases) and unstructured sources, PDFs, scanned documents, emails, invoices, Audio, Video, logs).

• Apply necessary document parsing and preprocessing techniques such as OCR, NER, NLP and AI techniques to extract information from unstructured formats

• Implement EDA, data cleaning, transformation, and enrichment processes to enable efficient implementation of AI/ML solutions, RAG, AI Agents, workflow automations and dashboards.

• Work with various data storage (RDBMS, NoSQL, Data Lakes/warehouses) solutions, ensuring efficient data querying and retrieval.

• Optimize data processing performance by improving query execution, indexing, and storage formats.

• Ability to use Gen AI technologies such as Vector DB, LLMs, RAG for the purpose of data engineering & AI/ML solution implementation

• Identify and resolve pipeline failures, data inconsistencies, and system bottlenecks.

• Ensure encryption, masking, and governance policies are applied to protect sensitive data.

• Work closely with AI/ML engineers, analysts, and business teams to define data needs and solutions.

• Implement automated testing, deployment pipelines, and best practices for data workflows.

• Maintain detailed documentation on data schemas, workflows, integration points and best practices.

• Stay updated with new technologies, AI/ML trends frameworks, and industry best practices to enhance data engineering capabilities.

Skills & Qualification Criteria

• 6–9 years of hands-on experience in designing, building, and managing scalable data pipelines.

• Proficiency in Python, Unix Shell scripting and SQL, (PostgreSQL, MySQL) or Java for data processing.

• Hands-on expertise in data science & data engineering methods and cloud platforms with expertise in managing large-scale unstructured & structured data. Cloud certifications are a plus.

• Hands-on experience in building and optimizing ETL/ELT pipelines using tools like Apache Airflow, or similar tools

• Experience with AWS (Glue, Redshift, S3) Document Extraction tools such as AWS Textract, or Azure.

• Strong experience with big data and distributed computing frameworks like Apache Spark, Hadoop or similar technologies

• Expertise in designing and managing data warehouses using platforms like Redshift, or BigQuery.

• Optimized database queries and stored procedures for improved performance of AWS Batch Jobs.

• Knowledge of real-time data processing tools like Apache Kafka, Flink, or Kinesis.

• Experience with Web scraping tools and frameworks (BeautifulSoup, Scrapy etc.) and NLP techniques and libraries like NLTK, SpaCy

• Experience with relational (PostgreSQL, MySQL) and NoSQL databases for ex. Dynamodb

• Ability to optimize SQL queries, storage formats (Parquet, ORC), and processing frameworks for efficiency.

• Strong ability to work with data scientists, ML engineers, and business teams to meet data needs.

• Understanding data privacy, compliance (GDPR, HIPAA), and best practices for secure data handling.

• Experience with DevOps practices

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