Infosys is hiring Technology lead in Data Engineer
Required Qualifications:
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Candidate must be located within commuting distance of Calgary or be willing to relocate to the area. This position may require travel in Canada.
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Bachelor’s degree or foreign equivalent required from an accredited institution. Will also consider three years of progressive experience in the specialty in lieu of every year of education.
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At least 4 years of Information Technology experience.
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Candidates authorized to work for any employer in the Canada without employer-based visa sponsorship are welcome to apply. Infosys is unable to provide immigration sponsorship for this role at this time.
Required Skills
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6+ years of overall technology experience with 3+ years in Contact Center and Conversational AI architecture.
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Data Pipelines: Build ETL flows for structured/unstructured data, ensuring normalization, deduplication, and semantic consistency.
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Vector Infrastructure: Manage pgvector, Azure AI Search, Redis vector indexing, and hybrid search layers.
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Data Governance: Implement zero-trust access, privacy controls, and compliance within AI context pipelines.
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Real-time Processing: Build event-driven architectures that continuously refresh embeddings and indexes. Required Qualifications
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Deep experience with distributed data systems, SQL, and orchestration tools.
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Experience tuning high-throughput database infrastructure.
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Knowledge of Google’s GECX is a plus.
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Familiarity with chunking strategies and embedding models. Skillset Requirements
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ETL & Data Modeling: Designing pipelines for structured/unstructured data, normalization, deduplication, and semantic consistency.
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Vector Databases: pgvector, Redis, Azure AI Search, hybrid search, and index optimization.
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Distributed Data Systems: Kafka, Spark, Flink, or similar event-driven architectures.
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Data Governance: Zero-trust access, privacy controls, compliance, and auditability.
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Real-time Embedding Updates: Event-driven refresh pipelines for RAG and agent memory systems.
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Chunking & Embeddings: Semantic chunking, metadata tagging, and embedding model selection.
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Search Infrastructure: BM25, hybrid search, inverted indexes, and ranking algorithms.
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Performance Tuning: High-throughput read/write optimization.
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Data Quality & Lineage: Validation, schema enforcement, and lineage tracking (e.g., Great Expectations, OpenLineage).
The job may also entail sitting as well as working at a computer for extended periods of time. Candidates should be able to effectively communicate by telephone, email, and face to face.
